GT1 Data Analysis and Knowledge Discovery

Sameer Antani's picture
Sameer Antani
U.S. National Library of Medicine / NIH (USA)
Agma J. M. Traina's picture
Agma J. M. Traina
University of Sao Paulo (BR)
Conference room
Session time
Thursday, June 22, 2017 - 14:00 to 15:30
A Tool for Optimizing De-Identified Health Data for Use in Statistical Classification
Abstract: 
When individual-level health data is shared in biomedical research the privacy of patients and probands must be protected. This is typically achieved with methods of data de-identification, which transform data in such a way that formal guarantees about the degree of protection from re-identification can be provided. In the process it is important to minimize loss of information to ensure that the resulting data is useful. A typical use case is the creation of predictive models for knowledge discovery and decision support, e.g. to infer diagnoses or to predict outcomes of therapies.
Fabian Prasser's picture
Fabian Prasser
TUM (DE)
Johanna Eicher's picture
Johanna Eicher
Raffael Bild's picture
Raffael Bild
Helmut Spengler's picture
Helmut Spengler
Klaus Kuhn's picture
Klaus Kuhn
A Recall Analysis of Core Word Lists over Children’s Utterances for Augmentative and Alternative Communication
Abstract: 
The vocabulary definition is of paramount importance in the customization of AAC devices, and it can be based on core word lists proposals. However, despite having the same purpose, there is no consensus among these core word lists. Therefore, in order to present evidence that helps to decide which list has a better recall, in this paper, 9 core word lists for children were reviewed; in addition, a Super List by merging these 9 lists was made.
Natalia Franco's picture
Natalia Franco
Federal University of Pernambuco (BR)
Augusto Lazzarotto Lima's picture
Augusto Lazzarotto Lima
Thiago Pinheiro Lima's picture
Thiago Pinheiro Lima
Edson Alves Silva's picture
Edson Alves Silva
Rinaldo José Lima's picture
Rinaldo José Lima
Robson Fidalgo's picture
Robson Fidalgo
Computational Analysis of BRCA1 Mutations in Pediatric Patients with Malignancies and Their Mothers
Abstract: 
Breast and ovarian cancers are the most prevalent amongst women. Similar incidence appear in childhood malignancies, where the basic ontogenetic mechanisms still remain to be elucidated. Such approaches, of relating mother’s cancer mutations with the prevalence of childhood cancer in their offspring could prove useful in the prognosis, early detection and therapy of childhood malignancies. The aim of the present study was to use computational and bioinformatics tools to investigate the incidence of mutations in mothers with children suffering from neoplasms.
George Lambrou's picture
George Lambrou
National Technical University of Athens (GR)
Ioanna Barbounaki's picture
Ioanna Barbounaki
Fotini Tzortzatou-Stathopoulou's picture
Fotini Tzortzatou-Stathopoulou
Ourania Petropoulou's picture
Ourania Petropoulou
Panagiotis Katrakazas's picture
Panagiotis Katrakazas
Dimitra Iliopoulou's picture
Dimitra Iliopoulou
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Multi-Label Modality Classification for Figures in Biomedical Literature
Abstract: 
The figures found in biomedical literature are a vital part of biomedical research, education and clinical decision. The multitude of their modalities and the lack of corresponding meta-data, constitute search and information retrieval a difficult task. We present multi-label modality classification approaches for biomedical figures. In particular, we investigate using both simple and compound figures for training a multi-label model to be used for annotating either all figures, or only those predicted as compound by an initial compound figure detection model.
Athanasios Lagopoulos's picture
Athanasios Lagopoulos
Aristotle University of Thessaloniki (GR)
Anestis Fachantidis's picture
Anestis Fachantidis
Grigorios Tsoumakas's picture
Grigorios Tsoumakas
Personalization of Infectious Disease Risk Prediction: Towards Automatic Generation of a Bayesian Network
Abstract: 
Infectious diseases have been a major cause of human morbidity, but most are avoidable. A relevant and accurate risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, current approaches are limited to the contexts and static risk prediction model. Thus, a dynamic and growing prediction model, based on Bayesian Network (BN), is proposed to overcome these limitations.
Retno Vinarti's picture
Retno Vinarti
Lucy Hederman's picture
Lucy Hederman
Trinity College Dublin (IE)
BREATH: Heat Maps Assisting the Detection of Abnormal Lung Regions in CT Scans
Abstract: 
Computed Tomography (CT) scans are often employed to diagnose lung diseases, as abnormal tissue regions may indicate whether proper treatment is required. However, detecting specific regions containing abnormalities in a CT scan demands time and effort of specialists. Moreover, different parts of a single lung image may present both normal and abnormal characteristics, what makes inaccurate the classification of a single lung as healthy (normal) or not.
Mirela T. Cazzolato's picture
Mirela T. Cazzolato
University of Sao Paulo - ICMC (BR)
Lucas C. Scabora's picture
Lucas C. Scabora
Alceu F. Costa's picture
Alceu F. Costa
Marcos R. Nesso-Jr's picture
Marcos R. Nesso-Jr
Luis F. Milano-Oliveira's picture
Luis F. Milano-Oliveira
Daniel S. Kaster's picture
Daniel S. Kaster
Caetano Traina-Jr's picture
Caetano Traina-Jr
University of Sao Paulo (BR)
Agma J. M. Traina's picture
Agma J. M. Traina
University of Sao Paulo (BR)

ST5 - Empowering Patients with Cancer through Connected Health

Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Brian Caulfield's picture
Brian Caulfield
Conference room
Session time
Thursday, June 22, 2017 - 14:00 to 14:45
iManageCancer:Empowering patients and strengthening self-management in cancer diseases
Abstract: 
Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient.
Haridimos Kondylakis's picture
Haridimos Kondylakis
Institute of Computer Science FORTH (GR)
Anca Bucur's picture
Anca Bucur
Feng Dong's picture
Feng Dong
Chiara Renzi's picture
Chiara Renzi
Andrea Manfrinati's picture
Andrea Manfrinati
Norbert Graf's picture
Norbert Graf
Stefan Hoffman's picture
Stefan Hoffman
Lefteris Koumakis's picture
Lefteris Koumakis
Gabriella Pravettoni's picture
Gabriella Pravettoni
Kostas Marias's picture
Kostas Marias
Manolis Tsiknakis's picture
Manolis Tsiknakis
Stephan Kiefer's picture
Stephan Kiefer
The Design of a Mobile App for Promotion of Physical Activity and Self-Management in Prostate Cancer Survivors: Personas, Feature Ideation and Low-Fidelity Prototyping
Abstract: 
"Most prostate cancer survivors are confronted with disease-related and treatment-related side effects that impact their quality of life. A tool that combines specific physical activity coaching with the promotion of a healthy lifestyle and self-management guidance might be a successful method to enhance a lifestyle change in these patients. As a prerequisite for useful health technology, it is important to consider a design process centred in the patients.
Francisco Monteiro-Guerra's picture
Francisco Monteiro-Guerra
Salumedia Tecnologias (SP)
Octavio Rivera-Romero's picture
Octavio Rivera-Romero
Vasiliki Mylonopoulou's picture
Vasiliki Mylonopoulou
Gabriel R. Signorelli's picture
Gabriel R. Signorelli
Oncoavanze (SP)
Francisco Zambrana's picture
Francisco Zambrana
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
The application of neuromuscular electrical stimulation (NMES) technologies in cancer care
Abstract: 
Despite the increase in long term cancer survivors, successful treatment is associated with significant sequelae. As a result, participation in voluntary exercise becomes difficult highlighting the need for pragmatic alternatives. Neuromuscular electrical stimulation (NMES) has been shown as effective in pathological conditions for improving muscle strength. However, its use in cancer care is sparse and has provided equivocal results. This paper outlines a proposed approach to the design, development, evaluation and implementation of NMES technology into cancer pathways.
Dominic OConnor's picture
Dominic OConnor
University College Dublin (IE)
Brian Caulfield's picture
Brian Caulfield

ST7 - MultiModal Interfaces for Natural Human Computer Interaction: Theory and Applications

Spiros Nikolopoulos's picture
Spiros Nikolopoulos
Centre for Research and Technology Hellas ITI-CERTH (GR)
Elisavet Chatzilari's picture
Elisavet Chatzilari
Centre for Research and Technology Hellas ITI-CERTH (GR)
Conference room
Session time
Thursday, June 22, 2017 - 14:45 to 15:30
An Error Aware SSVEP-based BCI
Abstract: 
ErrPs have been used lately in order to improve several existing BCI applications. In our study we investigate the contribution of ErrPs in a SSVEP based BCI. An extensive study is presented in order to discover the limitations of the proposed scheme. Using Common Spatial Patterns and Random Forests we manage to show encouraging results regarding the incorporation of ErrPs in a SSVEP system. Finally, we provide a novel methodology (ICRT) that can measure the gain of a BCI system by incorporating ErrPs in terms of time efficiency.
Fotis Kalaganis's picture
Fotis Kalaganis
CERTH/ITI (GR)
Elisavet Chatzilari's picture
Elisavet Chatzilari
Centre for Research and Technology Hellas ITI-CERTH (GR)
Kostas Georgiadis's picture
Kostas Georgiadis
Spiros Nikolopoulos's picture
Spiros Nikolopoulos
Centre for Research and Technology Hellas ITI-CERTH (GR)
Nikos Laskaris's picture
Nikos Laskaris
Ioannis Kompatsiaris's picture
Ioannis Kompatsiaris
Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing
Abstract: 
Gaze-based virtual keyboards allow people with motor disability a method for text entry by eye movements. The effectiveness and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystroke saving, error rate, accuracy, etc. However, in comparison to the conventional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition.
Korok Sengupta's picture
Korok Sengupta
University of Koblenz (DE)
Jun Sun's picture
Jun Sun
Raphael Menges's picture
Raphael Menges
Institute for Web Science and Technologies (DE)
Chandan Kumar's picture
Chandan Kumar
University of Koblenz (DE)
Steffen Staab's picture
Steffen Staab
Assessing the Usability of Gaze-Adapted Interface against Conventional Eye-Based Input Emulation
Abstract: 
In recent years, eye tracking systems have greatly improved, beginning to play a promising role as an input medium. Eye trackers can be used for application control either by simply emulating the mouse device in the traditional graphical user interface, or by customized interfaces for eye gaze events. In this work we evaluate these two approaches to assess their impact in usability. We present a gaze-adapted Twitter application interface with direct interaction of eye gaze input, and compare it to the Twitter in a conventional browser interface with gaze-based mouse and keyboard emulation.
Chandan Kumar's picture
Chandan Kumar
University of Koblenz (DE)
Raphael Menges's picture
Raphael Menges
Institute for Web Science and Technologies (DE)
Steffen Staab's picture
Steffen Staab

GT2 Decision Support Systems and Methods

Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Conference room
Session time
Thursday, June 22, 2017 - 16:00 to 17:15
EEG Signal Analysis of Real-word Reading and Nonsense-word Reading between Adults with Dyslexia and without Dyslexia
Abstract: 
The evolution in technology plays a major role in improving diagnostic accuracies. Pattern recognition and classification are techniques that may help uncover answers that are not always obvious. This paper attempts to discover such patterns found in brain wave signals in people with dyslexia using classifiers. Electroencephalogram (EEG) signals captured during real-word and nonsense-word reading activities from individuals with dyslexia are compared with normal controls.
Harshani Perera's picture
Harshani Perera
Murdoch University (AU)
Mohd Fairuz Shiratuddin's picture
Mohd Fairuz Shiratuddin
Kok Wai Wong's picture
Kok Wai Wong
Kelly Fullarton's picture
Kelly Fullarton
Emotional state recognition using advanced machine learning techniques on EEG data
Abstract: 
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning techniques, performing feature selection and classification into two at a time emotional states.
Katerina Giannakaki's picture
Katerina Giannakaki
University of Crete (GR)
Giorgos Giannakakis's picture
Giorgos Giannakakis
Christina Farmaki's picture
Christina Farmaki
Vangelis Sakkalis's picture
Vangelis Sakkalis
Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques
Abstract: 
The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied.
Evanthia Tripoliti's picture
Evanthia Tripoliti
Theofilos Papadopoulos's picture
Theofilos Papadopoulos
Georgia Karanasiou's picture
Georgia Karanasiou
FORTH (GR)
Fanis Kalatzis's picture
Fanis Kalatzis
Yorgos Goletsis's picture
Yorgos Goletsis
Aris Bechlioulis's picture
Aris Bechlioulis
Silivia Ghimenti's picture
Silivia Ghimenti
Tommaso Lomonaco's picture
Tommaso Lomonaco
Francesca Bellagambi's picture
Francesca Bellagambi
Roger Fuoco's picture
Roger Fuoco
Mario Marzilli's picture
Mario Marzilli
Maria Chiara Scali's picture
Maria Chiara Scali
Katerina Naka's picture
Katerina Naka
Abdelhamid Errachid's picture
Abdelhamid Errachid
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Exploiting active microRNA interactions for diagnosis from expression profiling experiments
Abstract: 
In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a learned model from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content.
Erdem Corapcioglu's picture
Erdem Corapcioglu
Hasan Ogul's picture
Hasan Ogul
Baskent University (TR)
The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks
Abstract: 
"Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations.
Agnese Marchesi's picture
Agnese Marchesi
University of Cassino and Southern Latium (IT)
Alessandro Bria's picture
Alessandro Bria
Claudio Marrocco's picture
Claudio Marrocco
Mario Molinara's picture
Mario Molinara
Francesco Tortorella's picture
Francesco Tortorella
University of Cassino and South Lazio (IT)
Jan-Jurre Mordang's picture
Jan-Jurre Mordang
Nico Karssemeijer's picture
Nico Karssemeijer

ST9 - Cloud Security and Data Privacy by Design

Iraklis Paraskakis's picture
Iraklis Paraskakis
SEERC (GR) & The University of Sheffield (UK)
Conference room
Session time
Thursday, June 22, 2017 - 16:00 to 17:15
Secure Database Outsourcing to the Cloud: Side-Channels, Counter-Measures and Trusted Execution
Abstract: 
"Outsourcing data processing and storage to the cloud is a persistent trend in the last years. Cloud computing offers many advantages like flexibility in resource allocation, cost reduction and high availability. However, when sensitive information is handed to a third party, security questions are raised since the cloud provider and his employees are not fully trusted. Standard security mechanisms like transport encryption and regular audits alone can't solve the issue of insider attacks. Additional cryptographic techniques are required. In this paper we build upon an existing
Matthias Gabel's picture
Matthias Gabel
Jeremias Mechler's picture
Jeremias Mechler
Karlsruhe Institute of Technology (DE)
Ontological Templates for Regulating Access to Sensitive Medical Data in the Cloud
Abstract: 
By embracing the cloud computing paradigm for storing and processing electronic medical records (EMRs), modern healthcare providers are able to realise significant cost savings. However, relinquishing control of sensitive medical data by delegating their storage and processing to third-party cloud providers naturally raises significant security concerns. One way to alleviate these concerns is to devise appropriate policies that infuse adequate access controls in cloud services.
Simeon Veloudis's picture
Simeon Veloudis
South East European Research Centre (SEERC) The University of Sheffield
Iraklis Paraskakis's picture
Iraklis Paraskakis
SEERC (GR) & The University of Sheffield (UK)
Yiannis Verginadis's picture
Yiannis Verginadis
Ioannis Patiniotakis's picture
Ioannis Patiniotakis
Gregoris Mentzas's picture
Gregoris Mentzas
HealthShare: Using Attribute-Based Encryption for Secure Data Sharing Between Multiple Clouds
Abstract: 
"In this paper, we propose HealthShare –a forwardlooking approach for secure ehealth data sharing between multiple organizations that are hosting patients’ data in different clouds. The proposed protocol is based on a Revocable Key-Policy Attribute-Based Encryption scheme and allows users to share encrypted health records based on a policy that has been defined by the data owner (i.e. patient, a member of the hospital, etc). Furthermore, access to a malicious or compromised user/organization can be easily revoked without the need to generate fresh encryption keys."
Antonis Michalas's picture
Antonis Michalas
University of Westminster (UK)
Noam Weingarten's picture
Noam Weingarten
Security in a Distributed Key Management Approach
Abstract: 
Cloud computing offers many advantages as flexibility or resource-efficiency and can significantly reduce costs. However, when sensitive medical data is outsourced to a cloud provider, classified records can leak. To protect the patients and application providers from a privacy breach data must be encrypted before it is uploaded. In this work, we present a distributed key management scheme that handles user-specific keys in a single-tenant scenario. The underlying database is encrypted and the secret key is only reconstructed temporarily in memory.
Gunther Schiefer's picture
Gunther Schiefer
Karlsruhe Institute of Technology (DE)
Murat Citak's picture
Murat Citak
Andreas Schoknecht's picture
Andreas Schoknecht
Matthias Gabel's picture
Matthias Gabel
Jeremias Mechler's picture
Jeremias Mechler
Karlsruhe Institute of Technology (DE)

Keynote Speech

Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Terry Poulton's picture
Terry Poulton
Saint George's University of London (UK)
Conference room
Session time
Thursday, June 22, 2017 - 17:30 to 18:15

The proposed lecture will present advanced achievements in the field of affective computing towards more enhanced human-computer-interaction interfaces, presenting advanced signal processing techniques and implementations applied to Electroencephalogram (EEG) recordings. In particular, the way emotions are 'reflected' in our brain signals and the way actions (both in explicit and implicit way, e.g., gestures in music) are combined with internal representations in our brain (involving mirror neuron system activation), will be presented and discussed. Moreover, potential implementations of the findings in the field of human assistive technology will be shown, including innovative ways of pain management, bullying identification, and Parkinson’s and Alzheimer's community support. 

Keynote Speech

Tony Solomonides's picture
Tony Solomonides
NorthShore University HealthSystem (USA)
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)
Conference room
Session time
Thursday, June 22, 2017 - 18:15 to 19:00

There are many diagnosis and treatment guidelines for certain conditions, but there are others, especially undifferentiated complaints, the problem of diagnosis is wide open. A team from NorthShore, Case Western Reserve, Carnegie Mellon, Weill Cornell, and Johns Hopkins is working on a joint project to study the diagnostic process in the case of several undifferentiated complaints, including (a) Abdominal Pain, and (b) Dizziness. There are many different approaches to the problem of discovery of good diagnostic pathways from the electronic health record. I will discuss several approaches and show some results from each. The problem of finding optimal pathways -- i.e. courses of action that would minimize time to diagnosis without an unacceptable risk of diagnostic error -- remains elusive, so it would give us something to discuss further.

GT3 Decision Support and Recommendation Systems

Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30
Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information
Abstract: 
An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac.
Fernando Garcia-Garcia's picture
Fernando Garcia-Garcia
ARTORG Center & University of Bern (CH)
Eleni Metaxa's picture
Eleni Metaxa
Stergios Christodoulidis's picture
Stergios Christodoulidis
Marios Anthimopoulos's picture
Marios Anthimopoulos
Nikolaos Kontopodis's picture
Nikolaos Kontopodis
Martina Correa-Londono's picture
Martina Correa-Londono
Thomas R. Wyss's picture
Thomas R. Wyss
Yannis Papaharilaou's picture
Yannis Papaharilaou
Christos V. Ioannou's picture
Christos V. Ioannou
Hendrik von Tengg-Kobligk's picture
Hendrik von Tengg-Kobligk
Stavroula Mougiakakou's picture
Stavroula Mougiakakou
A non-invasive medical decision support prototype system for Dermatology based on electrical impedance spectroscopy (DermaSense)
Abstract: 
Premature detection of malignant melanoma remains the primary factor in reducing mortality from this form of cancer. During the last decade diagnostic sensitivity and specificity have improved through the utilization of new computer-based technologies, which help improve lesion selection for pathology review and biopsy. Despite these advances in melanoma diagnosis, initial detection, timely recognition and quick treatment of melanoma remain crucial.
Alexander Zogkas's picture
Alexander Zogkas
Sotiria Gilou's picture
Sotiria Gilou
Aristotle University of Thessaloniki (GR)
Inessa Kirsanidou's picture
Inessa Kirsanidou
Chrysovalantis Korfitis's picture
Chrysovalantis Korfitis
Christina Kemanetzi's picture
Christina Kemanetzi
Elizabeth Lazaridou's picture
Elizabeth Lazaridou
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Is a Decision Support System Based on Robson's Classification Enough to Reduce Cesarean Section Rates?
Abstract: 
The cesarean section (CS) rates are important global indicators for measuring the access to obstetric services. In 2001, Robson proposed a CS classification in ten-groups as the most appropriate to compare surgery rates. However, having a decisional support system from Robson's Classification is enough to reduce CS rates? The births analysis that occurred in 2016, inside a public hospital maternity, showed 1,946 deliveries of which 35.7% were CS with a positive growth trend (R2 = 0.137).
Juliano Gaspar's picture
Juliano Gaspar
Universidade Federal de Minas Gerais (BR)
Zilma Reis's picture
Zilma Reis
Juliana Barra's picture
Juliana Barra
Design and Development of a Mobile Decision Support System: Guiding Clinicians Regarding Law in the Practice of Psychiatry in Emergency Department
Abstract: 
Decision-making in an emergency department needs to be efficient. It does not allow observation of the patient for a prolonged period of time, especially if the patients harm themselves or others, or refuses treatment. This includes suicidal, violent, intentional self-inflicted or non-consenting to treatments’ patient. Clinicians have to quickly decide whether to call the police, admit the patient to the psychiatric ward, according to recommended, predefined procedures.
Soudabeh Khodambashi's picture
Soudabeh Khodambashi
Norwegian University of Science and Technology (NO)
Florentin Moser's picture
Florentin Moser
Jon Atle Gulla's picture
Jon Atle Gulla
Pekka Abrahamsson's picture
Pekka Abrahamsson
Integrated decision support by combining textual information extraction, facetted search and information visualisation
Abstract: 
This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals.
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Hans-Jurgen Profitlich's picture
Hans-Jurgen Profitlich
Predicting Sepsis Biomarker Progression under Therapy
Abstract: 
Sepsis is a serious, life-threatening condition that presents a growing problem in medicine and health-care. It is characterized by quick progression and high variability in the disease manifestation, so treatment should be personalized and tailored to fit individual characteristics of a particular subject. That requires close monitoring of the patient's state and reliable predictions of how the targeted therapy will affect sepsis progression over time.
Ivan Stojkovic's picture
Ivan Stojkovic
Temple University (USA)
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30

Exhibition & Posters I

Conference room
Session time
Friday, June 23, 2017 - 09:00 to 18:45
Simulation for Training Cochlear Implant Electrode Insertion
Abstract: 
Cochlear implant surgery is performed to restore hearing in patients with a range of hearing disorders. To optimise hearing outcomes, trauma during the insertion of a cochlear implant electrode has to be minimised. Factors that contribute to the degree of trauma caused during surgery include: the location of the electrode, type of electrode, and the competence level of the surgeon. Surgical competence depends on knowledge of anatomy and experience in a range of situations, along with technical skills.
Xingjun Ma's picture
Xingjun Ma
The University of Melbourne (AU)
Sudanthi Wijewickrema's picture
Sudanthi Wijewickrema
University of Melbourne (AU)
Yun Zhou's picture
Yun Zhou
Bridget Copson's picture
Bridget Copson
James Bailey's picture
James Bailey
Gregor Kennedy's picture
Gregor Kennedy
Stephen OLeary's picture
Stephen OLeary
Towards an effective and efficient learning for biomedical data classification
Abstract: 
Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose expertise degree affects the accuracy of their diagnosis. Besides, due to the huge volume of data, it is a tiresome process.
Guilherme Camargo's picture
Guilherme Camargo
Rafael Staiger Bressan's picture
Rafael Staiger Bressan
Federal University of Technology Paraná
Pedro Henrique Bugatti's picture
Pedro Henrique Bugatti
Priscila Tiemi Maeda Saito's picture
Priscila Tiemi Maeda Saito
Dynamical Modelling of Pulmonary Compartment Response Using Differential Approach
Abstract: 
The article is focused on modeling and simulation of pulmonary compartment behaving and response during gas flowing in the lungs. The pulmonary system model includes the two ways: derivation of mathematical model for individual parts respiratory system, and numerical simulation of these parts. Dynamical model behavior is based on the set of differential equations allowing for dynamical description of entire pulmonary compartment. These simulations are especially important for understanding pulmonary system during gas cycle, and also serve for prediction of behaving pulmonary diseases.
Jan Kubicek's picture
Jan Kubicek
VSB-TU Ostrava (CZ)
Martin Augustynek's picture
Martin Augustynek
Marek Penhaker's picture
Marek Penhaker
Dissimilarity Measure of Consecutive Frames in Wireless Capsule Endoscopy Videos: a way of searching for abnormalities
Abstract: 
In a previous work we have shown that the curve representing the dissimilarity measure between consecutive frames of a wireless capsule endoscopic video of the small bowel, obtained by means of an image registration method, can be regarded as a rough indicator of the speed of the capsule, and simultaneously, it is also a valuable auxiliary medical tool.
Isabel Figueiredo's picture
Isabel Figueiredo
University of Coimbra (PT)
Carlos Leal's picture
Carlos Leal
Luis Pinto's picture
Luis Pinto
Pedro Figueiredo's picture
Pedro Figueiredo
Richard Tsai's picture
Richard Tsai
Wavelet based classification of epileptic seizures in EEG signals
Abstract: 
Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the world’s population with 1/3 of the epileptic patients not corresponding to anti-epileptic medication, epilepsy is constantly under the microscope and systems for automated detection of seizures are thoroughly examined.
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Measuring Steatosis in Liver Biopsies using Machine Learning and Morphological Imaging
Abstract: 
Non-Alcohol Liver Disease (NAFLD) is nowadays the most common liver disease in Western Countries. It is the chronic condition of fat expansion in liver, which is not associated with alcohol consumption. Quantitating steatosis in liver biopsies could provide objective measurement of the severity of the disease, instead of using semi-quantitative scoring systems. The current work, introduces an automated method for measuring steatosis in liver biopsies, using both machine learning and classical image processing techniques.
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Roberta Forlano's picture
Roberta Forlano
Maria Vavva's picture
Maria Vavva
Evaggelos C. Karvounis's picture
Evaggelos C. Karvounis
Maria Tsimplakidou's picture
Maria Tsimplakidou
Pinelopi Manousou's picture
Pinelopi Manousou
Longitudinal Monitoring and Detection of Alzheimer's Type Dementia from Spontaneous Speech Data
Abstract: 
A method for detection of Alzheimer's type dementia though analysis of vocalisation features that can be easily extracted from spontaneous speech is presented. Unlike existing approaches, this method does not rely on transcriptions of the patient's speech. Tests of the proposed method on a data set of spontaneous speech recordings of Alzheimer's patients (n=235) and elderly controls (n=242) show that accuracy of 68% can be achieved with a Bayesian classifier operating on features extracted through simple algorithms for voice activity detection and speech rate tracking.
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
A SIMULATION ENVIRONMENT FOR ACTIVE ENDOSCOPIC CAPSULES
Abstract: 
The best way for researchers to test their algorithms and design concepts, before experimenting on real human beings, is to create simulation environment platforms. In this paper, a virtual simulator for active endoscopic capsules is proposed. The simulator intends to provide researchers with an environment to test their vision and navigation algorithms applied to endoscopic capsule applications. The proposed simulation was created using Gazebo simulator under Robotic Operating System (ROS) environment.
Yasmeen Abu-Kheil's picture
Yasmeen Abu-Kheil
Khalifa University of Science and Technology (AE)
Lakmal Seneviratne's picture
Lakmal Seneviratne
Jorge Dias's picture
Jorge Dias
Differential diagnosis listing as relevance feedback: An essential user interface for clinical decision support systems
Abstract: 
"The user interface of the CDSS has not been well addressed by the field of research. In the previous study, we investigated the presentation of diagnostic output and proposed the hierarchical representation of differential diagnosis lists that provides an effective way of interfacing with a CDSS. To improve the input process, in this study, we proposed the incorporation of relevance feedback. Such feedback can be used to sharpen the search query, and to initiate a dialogue with the user, regarding the presence or absence of particular diagnostic information.
Takashi Okumura's picture
Takashi Okumura
National Institute of Public Health (JP)
Tomoko Kajiyama's picture
Tomoko Kajiyama
Noboru Sonehara's picture
Noboru Sonehara
Application of Evolutionary Algorithms on Unsupervised Segmentation of Lymphoma Histological Images
Abstract: 
Histological images analysis is widely used to carry out diagnoses of different types of cancer. Digital image processing methods can be used for this purpose, leading to more objective diagnoses. Segmentation techniques are applied in order to identify cellular structures capable of indicating the incidence of diseases. In addition, the extracted features from these specific regions can aid pathologists in diagnoses decision using classification techniques.
Thaina A. A. Tosta's picture
Thaina A. A. Tosta
Federal University of ABC
Marcelo Z. Do Nascimento's picture
Marcelo Z. Do Nascimento
Paulo R. Faria's picture
Paulo R. Faria
Leandro A. Neves's picture
Leandro A. Neves
Recognizing ureter and uterine artery in endoscopic images using a convolutional neural network
Abstract: 
Though endoscope-based surgery is highly beneficial regarding the recovery of the patients, it may also raise difficult tasks during special surgical actions. The main disadvantage is the lack of the tactile information for the surgeon, who consequently has to rely on the visual information only. As a special scenario, some organs of the human body, like ureters and arteries, have similar visual appearances, so distinguishing them is very challenging in real-time endoscopic surgery in these days.
Balazs Harangi's picture
Balazs Harangi
University of Debrecen (HU)
Andras Hajdu's picture
Andras Hajdu
University of Debrecen (HU)
Rudolf Lampe's picture
Rudolf Lampe
Peter Torok's picture
Peter Torok
Towards Patterns for Defining and Changing Data Collection Instruments in Mobile Healthcare Scenarios
Abstract: 
Especially in healthcare scenarios or clinical trials, a huge amount of data needs to be collected in rather short time. In this context, smart mobile devices can be a powerful instrument to foster data collection scenarios. To enable domain experts to create and maintain mobile data collection applications themselves, the QuestionSys framework uses a model-driven approach to digitize paper-based questionnaires. This digital transformation, in turn, is based on manual as well as automated tasks. The manual tasks applied by the domain experts are called change patterns.
Johannes Schobel's picture
Johannes Schobel
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Marc Schickler's picture
Marc Schickler
Manfred Reichert's picture
Manfred Reichert
On the Ontological Modelling of Co-Medication and Drug Interactions in Medical Cancer Therapy Regimens for a Clinical Decision Support System
Abstract: 
In an ongoing project aiming at a comprehensive AI-based tool to support clinical decisions in medical cancer therapy, the ontology OCTA is being developed. Its purpose is to provide general knowledge about active ingredients, therapy regimens, etc. that can be used by such a clinical decision support system. In this paper, we present a new extension of OCTA modelling co-medication and drug interactions, enabling the answering of queries relevant for medical decision making dealing with these aspects.
Christoph Beierle's picture
Christoph Beierle
University of Hagen (DE)
Bettina Sader's picture
Bettina Sader
Christian Eichhorn's picture
Christian Eichhorn
Gabriele Kern-Isberner's picture
Gabriele Kern-Isberner
Ralf Georg Meyer's picture
Ralf Georg Meyer
Mathias Nietzke's picture
Mathias Nietzke
An IT Platform Enabling Remote Therapeutic Interventions
Abstract: 
The development of IT solutions for properly supporting homework in the context of therapeutic interventions has been neglected so far. Both psychologists and medical doctors crave for the widespread use of smart mobile devices (e.g., smartphones) to assist patients in performing homework. If a patient is assisted by a smart mobile device, for example, the latter can automatically inform the therapist of the homework outcome and, hence, enable him to timely adjust the therapeutic intervention if required.
Marc Schickler's picture
Marc Schickler
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Michael Stach's picture
Michael Stach
Johannes Schobel's picture
Johannes Schobel
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Berthold Langguth's picture
Berthold Langguth
Manfred Reichert's picture
Manfred Reichert
Laugh and crying perception in patients with severe and moderate TBI using FFT analysis
Abstract: 
Post-traumatic emotional disorder event is a major cause of slow rehabilitation worldwide. The present study investigates the effects of emotional sound stimulation at different groups of subjects with TBI. At the base of our research strategy, we had used a group with diffuse axonal injury (DAI). We collected three groups of patients: 45 healthy adults, 20 patients with severe TBI and 19 patients with moderate TBI. Neurophysiology trials aim was to determine the processes that accompany emotional changes within traumatic brain damage.
Galina Portnova's picture
Galina Portnova
IHNA (RU)
Kseniya Gladun's picture
Kseniya Gladun
Detection and Management of Depression in Cancer Patients using Augmented Reality Technologies, Multimodal Signal Processing and Persuasive Interfaces
Abstract: 
This visual paper aims at proposing a framework for detecting depression in cancer patients using prosodic and statistical features extracted by speech, while chatting with an augmented reality virtual coach.
Alexandros Roniotis's picture
Alexandros Roniotis
Haridimos Kondylakis's picture
Haridimos Kondylakis
Institute of Computer Science FORTH (GR)
Manolis Tsiknakis's picture
Manolis Tsiknakis
Neural Networks Modelling after Myocardial Infarction in Rats
Abstract: 
Cardiac function is reduced after acute myocardial infarction due to myocardial injury and to changes in the viable non-ischemic myocardium, a process known as cardiac remodeling. Current treatment of patients with acute myocardial infarction (AMI) reduces infarct size, preserves left ventricular function, and improves survival. However, it does not prevent remodeling which leads to heart failure. The aim of the present study was to model the echocardiographically estimated data with respect to the surgically collected data using Neural Networks.
Ioanna Iliopoulou's picture
Ioanna Iliopoulou
Iordanis Mourouzis's picture
Iordanis Mourouzis
George Lambrou's picture
George Lambrou
National Technical University of Athens (GR)
Constantinos Pantos's picture
Constantinos Pantos
Dimitra Iliopoulou's picture
Dimitra Iliopoulou
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Automatic Segmentation of Melanoma in Dermoscopy Images using Fuzzy Numbers
Abstract: 
Melanoma is the most dangerous type of skin cancer, but when treated in its early stages the chance of cure is increased. However, the detection of melanoma is a challenging task even for specialists due to low contrast of skin lesions and presence of artifacts. Therefore, developing an automatic segmentation tool for skin lesion analysis using dermoscopy images is a critical step for improving the diagnosis. This work proposes an automatic melanoma segmentation approach, based on Fuzzy Numbers.
Filipe Rolim Cordeiro's picture
Filipe Rolim Cordeiro
Federal Rural University of Pernambuco (BR)
Jessica Barbosa Diniz's picture
Jessica Barbosa Diniz
Random Walker with Fuzzy Initialization Applied to Segment Masses in Mammography Images
Abstract: 
Segmentation of masses in mammography images is an important task in early detection of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. In this work, we propose an improvement on Random Walker algorithm to segment masses, by applying a fuzzy approach in the initialization stage. We evaluated the new approach compared with classical Random Walker, using 57 images of Mini-MIAS database.
Filipe Rolim Cordeiro's picture
Filipe Rolim Cordeiro
Federal Rural University of Pernambuco (BR)
Kallebe F. P. Bezerra's picture
Kallebe F. P. Bezerra
Wellington Pinheiro Dos Santos's picture
Wellington Pinheiro Dos Santos
Self-Adaptive Multi-Objective Evolutionary Algorithm for Molecular Design
Abstract: 
Self-adaptation is an efficient way to control the strategy parameters of an EA automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed SAMOEA is a two level algorithm. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on a MOGA implementation. The inner level consists of eMEGA instances.
Christos Kannas's picture
Christos Kannas
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
Towards a Conceptual Framework Model Fostering Process Comprehension in Healthcare
Abstract: 
Despite the widespread use of process models in healthcare organizations, there are many unresolved issues regarding the reading and comprehension of these models by domain experts. This is aggravated by the fact that there exists a plethora of process modeling languages for the graphical documentation of processes, whose use is often not consistent for various reasons. Hence, the identification of those factors fostering the comprehension of process models becomes crucial.
Michael Zimoch's picture
Michael Zimoch
Ulm University (DE)
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Thomas Probst's picture
Thomas Probst
Winfried Schlee's picture
Winfried Schlee
Manfred Reichert's picture
Manfred Reichert
ICE: Interactive Classification Rule Exploration on Epidemiological Data
Abstract: 
Personalized medicine benefits from the identification of subpopulations that exhibit higher prevalence of a disease than the general population: such subpopulations can become the target of more intensive investigations to identify risk factors and to develop dedicated therapies. Classification rule discovery algorithms are an appropriate tool for discovering such subpopulations: they scale well, even for multi-dimensional data, are not affected by missing values, and deliver comprehensible patterns.
Miro Schleicher's picture
Miro Schleicher
Till Ittermann's picture
Till Ittermann
Uli Niemann's picture
Uli Niemann
University of Magdeburg (DE)
Henry VOlzke's picture
Henry VOlzke
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
A digital pen-pased tool for instant digitisation and digitalisation of biopsy protocols
Abstract: 
In order to improve medical processes in nephrology, we present an application that allows doctors to create biopsy protocols by using a digital pen on a tablet. The biopsy protocol app is seamlessly integrated into existing infrastructure at the hospital (see figure 1). Compared to other reporting tools, we provide (1) real-time hand-writing/gesture recognition and real-time feedback on the recognition results on the screen; (2) a real-time digitisation into structured data and PDF documents; and (3) the mapping of the transcribed contents into concepts of the Banff classification.
Alexander Prange's picture
Alexander Prange
Danilo Schmidt's picture
Danilo Schmidt
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Travelers’ Perceptions about m-Health Technology
Abstract: 
The aim of this paper is to present the perceptions of the Travelers about the m-Health Technology. An empirical study was conducted to record travelers’ mobile technology usage behavior and their perceptions about a proposed idea for an m-Health application. A pilot mobile application which informs travelers for health issues automatically, according to their current location was developed based on the finding of the study and evaluated by a small group of users.
Parisis Gallos's picture
Parisis Gallos
National and Kapodistrian University of Athens
John Mantas's picture
John Mantas
Lesion Segmentation in Dermoscopy Images Using Particle Swarm Optimization and Markov Random Field
Abstract: 
Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologist’s in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can be very helpful to facilitate the early detection. The automated detection of the lesion borders is one of the most important steps in dermoscopic image analysis.
Khalid Eltayef's picture
Khalid Eltayef
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu
A Low Bit Rate Wearable Motion Sensing Platform for Gesture Classification
Abstract: 
A body motion sensing platform is presented. The target application for this platform is gesture recognition. The platform consists of wearable sensor nodes built using off-the-shelf components. Each sensor node includes inertial sensors as well as a low- power microcontroller and a 2.4 GHz radio transceiver. Signal classifiers such as Fisher’s linear discriminant classifiers, static neural networks and focused time delay neural networks (fTDNN) are employed to classify the signals obtained from the wearable sensor nodes.
Fahad Moiz's picture
Fahad Moiz
Walter D. Leon-Salas's picture
Walter D. Leon-Salas
Reza Derakhshani's picture
Reza Derakhshani
University of Missouri-Kansas City (USA)
Yugyung Lee's picture
Yugyung Lee
An innovative gaming approach to prevent anxiety disorders and promote youth resilience
Abstract: 
As the stressed adolescents of today are the adults of tomorrow, it is important to prevent and diagnose early anxiety disorders and enhance overall self esteem and wellbeing of the youth. A novel intervention for Generalized Anxiety Disorders in adolescents is proposed, that incorporates current clinical guidelines and state-of-the art ICT technology.
Vassilia Costarides's picture
Vassilia Costarides
National Technical University of Athens (GR)
Athanasios Anastasiou's picture
Athanasios Anastasiou
Ioannis Kouris's picture
Ioannis Kouris
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Ecoepidemiological Simulation as a Serious Game Engine Module
Abstract: 
The integration of an agent-based simulation model as a component of a game engine for serious games targeting prevention and health promotion in the context of infectious diseases is described. It is argued that a combination of agent-based modelling and serious games can help provide a more realistic picture of disease spread than conventional ecoepidemiological models by facilitating the integration of more detailed multidisciplinary expert knowledge.
Masood Masoodian's picture
Masood Masoodian
Aalto University (FI)
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
WeightBit : an advancement in wearables technology
Abstract: 
Wearable devices are becoming an important interface between users and fitness activities. Their capabilities are improving exponentially and new strategies are being developed to track sports using sensors that are widely used in robotics. In combination with these wearable gadgets, smartphone applications are normally created to allow the user to visualise data, share data through social networks, or compete with other users. The technology behind these devices is often simple and uses sensors that can be found in a smartphone, such as GPS, accelerometer and gyroscope.
Dario Guida's picture
Dario Guida
University of Westminster (UK)
Artie Basukoski's picture
Artie Basukoski
Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning
Abstract: 
Tumor infiltrating immune cells in cancer, such as lymphocytes, have been studied in last years and evidence shows them to be related with the disease prognosis. Knowing the distribution and localization of these cells is of especial interest for pathologists and nowadays involves manual counting on Immunohistochemistry (IHC) Images. This work presents a model based on Deep Convolutional Neural Networks to automatically detect and count lymphocytes on IHC Images of gastric cancer as well as an lymphocyte images database for future work.
Emilio Garcia's picture
Emilio Garcia
Renato Hermoza's picture
Renato Hermoza
Pontifical Catholic University of Peru (PE)
Cesar Beltran Castanon's picture
Cesar Beltran Castanon
Pontificia Universidad Católica del Perú (PE)
Luis Cano's picture
Luis Cano
Miluska Castillo's picture
Miluska Castillo
Carlos Castanñeda's picture
Carlos Castanñeda
Predicting Comorbidities using Resampling and Dynamic Bayesian Networks with Latent Variables
Abstract: 
Comorbidities such as hypertension and lipid metabolism are often associated in diseases such as diabetes, and the early prediction of these is of great value when trying to manage progression. This is the start of a project to model multiple comorbidities in diabetes using dynamic Bayesian networks with latent variables in order to stratify patient cohorts. In this paper, we demonstrate some initial results on a dataset where the class imbalance problem poses an issue due to the rare occurrence of different individual comorbidities on a visit-by-visit basis.
Leila Yousefi's picture
Leila Yousefi
Lucia Saachi's picture
Lucia Saachi
Riccardo Bellazzi's picture
Riccardo Bellazzi
Luca Chiovato's picture
Luca Chiovato
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)
Automatic extraction of breast cancer information from clinical reports
Abstract: 
The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features.
Claudia Breischneider's picture
Claudia Breischneider
Sonja Zillner's picture
Sonja Zillner
Siemens AG (DE)
Matthias Hammon's picture
Matthias Hammon
Paul Gass's picture
Paul Gass
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Toward a Network-based Approach to Modeling Epistatic Interactions in Genome-wide Association Studies
Abstract: 
In genome-wide association studies, genetic variants can be analyzed within statistical frameworks that take the underlying genetic model into consideration (e.g., the Cochran–Armitage test for trend or the logistic regression model). Should a researcher was to investigate the role played by the interaction between two or more genetic variants (epistasis), it would suffice to add interaction terms to a logistic regression model, where these terms correspond to the product of the genetic variants. However, such a model would not capture the subtlety of the interactions in the human genome.
Camilo Palazuelos's picture
Camilo Palazuelos
University of Cantabria (SP)
Marta Zorrilla's picture
Marta Zorrilla
Javier Llorca's picture
Javier Llorca
Relevant lifelong nutrition information for the prevention and treatment of childhood obesity – Design and creation of new openEHR archetype set
Abstract: 
"Introduction: The worldwide prevalence of obesity had a drastic increase in childhood/ adolescents. In 2014, it was estimated that 41 million children in all world, under the age of 5 were overweight or obese. Despite all the efforts, the number of overweight and obese children/adolescents is rising in almost all countries. Meanwhile, some points impaired the healthcare professionals to follow obesity guideline clinical practice (CPG). Information systems have an important role to play in the maintaining of this reality.
Priscila Maranhão's picture
Priscila Maranhão
Gustavo Marísio Bacelar-Silva's picture
Gustavo Marísio Bacelar-Silva
Duarte Nuno Gonçalves-Ferreira's picture
Duarte Nuno Gonçalves-Ferreira
Pedro Vieira-Marques's picture
Pedro Vieira-Marques
Ricardo João Cruz-Correia's picture
Ricardo João Cruz-Correia
Quality of information about Physical Activity in Breast Cancer Facebook Pages. A Preliminary Content Review
Abstract: 
INTRODUCTION: Adherence to physical activity might be the most important behavior associated with lower mortality and higher quality of life in cancer survivors. Presencial support groups are valuable and have been largely recommended, however, it may not be available for all patients. As an alternative, it is common that many women access Internet and social media resources looking for information, advice, and support in blogs, chat groups, Facebook, and Twitter.
Gabriel R. Signorelli's picture
Gabriel R. Signorelli
Oncoavanze (SP)
Matilde M. Fernandez's picture
Matilde M. Fernandez
Francisco Monteiro-Guerra's picture
Francisco Monteiro-Guerra
Salumedia Tecnologias (SP)
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Brian Caulfield's picture
Brian Caulfield
Method for intra-surgical phase detection by using real-time medical device data
Abstract: 
"The analysis of surgical activities became a popular field of research in recent years and various methods have been published to detect surgical phases from manifold data sources in operating rooms. Goal of this research is to develop a method for extracting realtime information of surgical activities. In this work we use fine-grained data of surgical devices and operating room equipment which is produced permanently during a surgery.
Norman Spangenberg's picture
Norman Spangenberg
Leipzig University (DE)
Christoph Augenstein's picture
Christoph Augenstein
Bogdan Franczyk's picture
Bogdan Franczyk
Martin Wagner's picture
Martin Wagner
Martin Apitz's picture
Martin Apitz
Hannes Kenngott's picture
Hannes Kenngott
Developing a collaborative knowledge system for Cancer Diseases
Abstract: 
In this paper we present a collaborative consultative knowledge system for cancer diseases. The «Worldwide Collaborative Consultative Knowledge System» aims to meet four goals. The first one is to be a tool which allows experts in a particular domain to develop a common knowledge base in a collaborative way. A second goal is the support of multiple knowledge bases. Each knowledge base is constructed to be used in a consultative way either from simple users or from experts of the domain offering high quality consultation services. In this paper we focus in cancer diseases.
Mihalis Giannoulis's picture
Mihalis Giannoulis
Emmanouil Marakakis's picture
Emmanouil Marakakis
Haridimos Kondylakis's picture
Haridimos Kondylakis
Institute of Computer Science FORTH (GR)
Studying the Potential of Multi-Target Classification to Characterize Combinations of Classes with Skewed Distribution
Abstract: 
The identification of subpopulations with particular characteristics with respect to a disease is important for personalized diagnostics and therapy design. But what if the manifestation of the disease is not described by one target variable but of many? Multi-target classification algorithms are the straightforward choice in this context and have been successfully applied in different application scenarios. However, most investigations do not focus on the effects of a skewed class distribution, where the prevalence of one of the multi-target combinations is more rare than the others.
Arne Schneck's picture
Arne Schneck
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Sven Kalle's picture
Sven Kalle
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Berthold Langguth's picture
Berthold Langguth
Manfred Reichert's picture
Manfred Reichert
Michael Landgrebe's picture
Michael Landgrebe
Conceptualising a Targeted Rehabilitation Exercise Biofeedback System for a Cancer Survivorship Population
Abstract: 
"Introduction The increased prevalence of cancer survivors requires a focus on developing long-term, cost-effective management strategies to prevent and limit disability and morbidity. Background Cancer survivors with pain, weakness and restricted movement often benefit from targeted exercise programmes provided by a Physiotherapist. Physical, psychological and situational factors can impact on patients’ abilities to complete these exercises.
Louise Brennan's picture
Louise Brennan
University College Dublin (IE)
Ailish Daly's picture
Ailish Daly
Brian Caulfield's picture
Brian Caulfield
Towards Flexible Remote Therapeutic Interventions
Abstract: 
During the last years, an increasing number of patients have been suffering from mental disorders, making the introduction of efficient therapies absolutely essential. In this context, therapeutic interventions with homework play a crucial role. In line with this, the widespread dissemination of smart mobile devices offers promising opportunities for the support of a variety of remote therapeutic interventions. First, smart mobile devices can assist patients in performing homework. Second, they may be used to monitor homework outcomes by therapists.
Marc Schickler's picture
Marc Schickler
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Johannes Schobel's picture
Johannes Schobel
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Manfred Reichert's picture
Manfred Reichert
Integrating a PACS Network to a Statewide Telemedicine System - A case study of the Santa Catarina State Integrated Telemedicine and Telehealth System
Abstract: 
In the literature, PACS and Telemedicine are located in different or complementary works. However, when integrated as part of the same solution, both contribute to the healthcare process as a whole. This work describes the experience on integrating a PACS infrastructure to a statewide telemedicine system, allowing to the latter to benefit from concepts defined by the former in terms of acquisition, transmission and storage of digital images.
Alexandre Savaris's picture
Alexandre Savaris
Alexandre Augusto Gimenes Marquez Filho's picture
Alexandre Augusto Gimenes Marquez Filho
Rodrigo Rodrigues Pires de Mello's picture
Rodrigo Rodrigues Pires de Mello
Gabriela Bussolo Colonetti's picture
Gabriela Bussolo Colonetti
Aldo von Wangenheim's picture
Aldo von Wangenheim
Dirk Krechel's picture
Dirk Krechel
RheinMain University of Applied Science (DE)
Efficiently Indexing Multiple Repositories of Medical Image Databases
Abstract: 
Performing content-based image retrieval over large repositories of medical images demands efficient computational techniques. The use of such techniques is intended to speed up the work of physicians, who often have to deal with information from multiple data repositories. When dealing with multiple data repositories, the common computational approach is to search each repository separately and merge the multiple results into one final response, which slows down the whole process.
Paulo H. Oliveira's picture
Paulo H. Oliveira
Lucas C. Scabora's picture
Lucas C. Scabora
Mirela T. Cazzolato's picture
Mirela T. Cazzolato
University of Sao Paulo - ICMC (BR)
Willian D. Oliveira's picture
Willian D. Oliveira
Agma J. M. Traina's picture
Agma J. M. Traina
University of Sao Paulo (BR)
Caetano Traina-Jr's picture
Caetano Traina-Jr
University of Sao Paulo (BR)

ST1 - Ambient Assisted Living based on IoT Technologies

Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Conference room
Session time
Friday, June 23, 2017 - 11:00 to 12:30
Applied technologies and Smart Home applications in the health sector
Abstract: 
Smart homes are no longer design concepts of the future. They are being built now, and they are having a direct impact on the lifestyles of people living in them. The aim of smart home systems is to create an environment that is aware of the activities taking place within it. Beside the healthy people, disabled people also need such systems to make their life easier, because they encounter with a lot of difficulties in their everyday life especially when they are at home.
Nikolaos Katsivelis's picture
Nikolaos Katsivelis
Athanasios Anastasiou's picture
Athanasios Anastasiou
Ourania Petropoulou's picture
Ourania Petropoulou
George Lambrou's picture
George Lambrou
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
A Fall Prevention System for the Elderly: Preliminary Results
Abstract: 
The fall prevention in the elderly population is a field of growing interest. This paper presents the preliminary results of a fall prevention system based on a customized exergame program. Results show that the participants involved in the experiments evaluate positively the system usability. Moreover, in order to evaluate the efficiency of the system, a global improvement of around 8.8% has been observed in the postural response after just two sessions with the system.
Giuseppe Palestra's picture
Giuseppe Palestra
StreamVision sas (FR)
Mohamed Rebiai's picture
Mohamed Rebiai
Estelle Corutial's picture
Estelle Corutial
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Indoor Localization: A Cost-Effectiveness vs. Accuracy Study
Abstract: 
"Recognizing the occupants’ movement and locations within a home is a basic functionality, underlying a variety of smart-home services, including energy management, ambient environment control, and assistive-living services for seniors and people with disabilities. The outdoor-localization variant of the problem is effectively addressed with the use of GPS; however, GPS does not work well inside buildings, which makes the indoor positioning problem a very active research topic.
Parisa Mohebbi's picture
Parisa Mohebbi
Eleni Stroulia's picture
Eleni Stroulia
University of Alberta (CA)
Ioanis Nikolaidis's picture
Ioanis Nikolaidis
University of Alberta (CA)
Quality of Context Evaluating Approach in AAL Environment using IoT Technology
Abstract: 
This paper presents an approach to the evaluation of Quality of Context (QoC) parameters in a ubiquitous Ambient Assisted Living (AAL) e-Health platform, supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. The proposal is initially verified with the Siafu simulator in an AAL scenario where the user’s health is monitored with information about blood pressure and body temperature. The research proceeded with the use of IoT technology, the e-Health Sensor Platform, a differentiated real environment.
Debora Cabral Nazario's picture
Debora Cabral Nazario
Pedro J. Campos's picture
Pedro J. Campos
Eduardo C. Inacio's picture
Eduardo C. Inacio
Federal University of Santa Catarina (BR)
Mario A. R. Dantas's picture
Mario A. R. Dantas
Robotic applications towards an interactive alerting system for medical purposes
Abstract: 
"Social consumer robots are slowly but strongly invading our everyday lives as their prices are becoming lower and lower, constituting them affordable for a wide range of civilians. There has been a lot of research concerning the potential applications of social robots, some of which may implement companionship or proxying technology-related tasks and assisting in everyday household endeavors, among others.
Konstantinos Panayiotou's picture
Konstantinos Panayiotou
Aristotle University of Thessaloniki (GR)
Sofia E. Reppou's picture
Sofia E. Reppou
George Karagiannis's picture
George Karagiannis
Emmanouil Tsardoulias's picture
Emmanouil Tsardoulias
Aristeidis G. Thallas's picture
Aristeidis G. Thallas
Andreas L. Symeonidis's picture
Andreas L. Symeonidis
HEAR?INFO: A Modern Mobile-Web Platform Addressed to Hard-of-Hearing Elderly Individuals
Abstract: 
In the concept of the hearing loss awareness, a modern mobile-web platform is hereby presented, aiming to offer constant online access to individuals who are hard of hearing, while presenting them regularly updated information concerning their condition. This information is presented via a specific modified interface, taking into account the special needs of the specific community. After a thorough research in GUI, the software requirements substitute or supplement the lack of integrated sound systems, with visual modifications, caption text and even specially chosen colours.
Penelope Ioannidou's picture
Penelope Ioannidou
National Technical University of Athens (GR)
Panagiotis Katrakazas's picture
Panagiotis Katrakazas
Stefanos Kollias's picture
Stefanos Kollias
Sarafidis Michail's picture
Sarafidis Michail
National Technical University of Athens (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 11:00 to 12:30
A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards
Abstract: 
Curriculum design and implementation in higher medical education can be a great challenge. Although there are well-defined standards, such as the Curriculum Inventory and Competency Framework by MedBiquitous Consortium, existing systems are incapable of a visual representation of the various components, attributes, and relations. In this paper, we present the MEDCIN platform, a pilot tool which uses a standard-compliant curriculum data model to offer comprehensive and thorough analysis of a given curriculum.
Martin Komenda's picture
Martin Komenda
Masaryk University (CZ)
Matej Karolyi's picture
Matej Karolyi
Masaryk University (CZ)
Christos Vaitsis's picture
Christos Vaitsis
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Luke Woodham's picture
Luke Woodham
St George's University of London (UK)

Keynote Speech

Nikos Chrisochoides's picture
Nikos Chrisochoides
Old Dominion University Norfolk Virginia (USA)
Anastasios Siountas's picture
Anastasios Siountas
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 12:30 to 13:15

In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in eloquent areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This talk reports the results (using clinical data) of a comparison of the accuracy and performance among four open-source non-rigid registration methods for handling brain deformation in the presence of tumor resection, including a new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby automatically handling deformation in the presence of resection.

 

In this talk data from 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. Three measures aid in assessing the accuracy of the registration methods: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Performance analysis showed that the adaptive method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting and significantly better than other readily-available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room.

 

Given time availability a brief description of related real-time Image-to-Mesh conversion technologies at developed CRTC to facility adaptive method will be presented.

 

GT4 Technology Enhanced Medical Education and Simulation

Iraklis Paraskakis's picture
Iraklis Paraskakis
SEERC (GR) & The University of Sheffield (UK)
Meni Tsitouridou's picture
Meni Tsitouridou
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Design and Evaluation of a Virtual Reality Simulation Module for Training Advanced Temporal Bone Surgery
Abstract: 
Surgical education has traditionally relied on cadaveric dissection and supervised training in the operating theatre. However, both these forms of training have become inefficient due to issues such as scarcity of cadavers and competing priorities taking up surgeons' time. Within this context, computer-based simulations such as virtual reality have gained popularity as supplemental modes of training.
Sudanthi Wijewickrema's picture
Sudanthi Wijewickrema
University of Melbourne (AU)
Bridget Copson's picture
Bridget Copson
Yun Zhou's picture
Yun Zhou
Xingjun Ma's picture
Xingjun Ma
The University of Melbourne (AU)
Robert Briggs's picture
Robert Briggs
James Bailey's picture
James Bailey
Gregor Kennedy's picture
Gregor Kennedy
Stephen OLeary's picture
Stephen OLeary
Inductive learning of the surgical workflow model through video annotations
Abstract: 
Surgical workflow modeling is becoming increasingly useful to train surgical residents for complex surgical procedures. Rule-based surgical workflows have shown to be useful to create context-aware systems. However, manually constructing production rules is a time-intensive and laborious task. With the expansion of new technologies, large video archive can be created and annotated exploiting and storing the expert’s knowledge.
Hirenkumar Nakawala's picture
Hirenkumar Nakawala
Politecnico di Milano (IT)
Elena De Momi's picture
Elena De Momi
Laura Erica Pescatori's picture
Laura Erica Pescatori
Anna Morelli's picture
Anna Morelli
Giancarlo Ferrigno's picture
Giancarlo Ferrigno
LiveBook: Competence Assessment with Virtual-Patient Simulations
Abstract: 
Virtual-patient simulators play an important role in modern medical education. These simulators provide a safe environment for learning, give contextual feedback to learners, and allow the learner to move beyond the time and space constraints of traditional face-to-face medical instruction. In this paper, we present an interactive simulation system, LiveBook. This system interacts with students in natural language, and provides detailed feedback on the student's performance after a case has been studied.
Sina Jalali's picture
Sina Jalali
Eleni Stroulia's picture
Eleni Stroulia
University of Alberta (CA)
Sarah Foster's picture
Sarah Foster
Sarah Forgie's picture
Sarah Forgie
Amit Persad's picture
Amit Persad
Diya Shi's picture
Diya Shi
Implementation of process-oriented feedback in a clinical reasoning tool for virtual patients
Abstract: 
Virtual patients (VPs) offer a safe environment to teach clinical reasoning skills, but feedback is often provided in outcome-, rather than process-oriented fashion. For complex cognitive skills, such as clinical reasoning, the process itself is often more important then the end result, especially during learning phase. We have developed a tool that can be integrated into VP systems to specifically support the clinical reasoning process and provide both, outcome- and process-oriented feedback.
Inga Hege's picture
Inga Hege
Ludwig-Maximilians-Universität München (DE)
Andrzej A Kononowicz's picture
Andrzej A Kononowicz
Michal Nowakowski's picture
Michal Nowakowski
Martin Adler's picture
Martin Adler
Novel Method for Storyboarding Biomedical Videos for Medical Informatics
Abstract: 
We propose a novel method for developing static storyboard for video clips included with biomedical research literature. The technique uses both visual and audio content in the video to select candidate key frames for the storyboard. From the visual channel, the intra-frames are extracted using FFmpeg tool. IBM Watson speech-to-text service is used to extract words from the audio channel, from which clinically significant concepts (key concepts) are identified using the U.S. National Library of Medicine‘s Repository for Informed Decision Making (RIDEM) service.
Sema Candemir's picture
Sema Candemir
Sameer Antani's picture
Sameer Antani
U.S. National Library of Medicine / NIH (USA)
Zhiyun Xue's picture
Zhiyun Xue
George Thoma's picture
George Thoma
A Proposed Learner Activity Taxonomy and a Framework for Analysing Learner Engagement versus Performance using Big Educational Data
Abstract: 
The inclusion of information and communication technologies in Healthcare and Medical Education is a fact nowadays. Furthermore numerous virtual learning environments have been established in order to host both educational material and learner’s online activities. Online modules in a VLE can be designed in very different ways being part of different types of courses, while different models can be used to design the course based on what the creator aims to achieve. Thus, the types and the importance of the different elements of the online course may vary a lot.
Stathis Th. Konstantinidis's picture
Stathis Th. Konstantinidis
University of Nottingham (UK)
Aaron Fecowycz's picture
Aaron Fecowycz
Kirstie Coolin's picture
Kirstie Coolin
Heather Wharrad's picture
Heather Wharrad
George Konstantinidis's picture
George Konstantinidis
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT5 Biomedical Signal

Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Towards multi-parametric hub scoring of functional cortical brain networks: An electroencephalographic (EEG) study across lifespan
Abstract: 
The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Ana Vivas's picture
Ana Vivas
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data
Abstract: 
Discriminating the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers.
Kostas M. Tsiouris's picture
Kostas M. Tsiouris
National Technical University of Athens (GR)
Vasileios C. Pezoulas's picture
Vasileios C. Pezoulas
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Michalis Zervakis's picture
Michalis Zervakis
Detection of Alcoholism based on EEG Signals and Functional Brain Network Features Extraction
Abstract: 
Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.
Negar Ahmadi's picture
Negar Ahmadi
Yulong Pei's picture
Yulong Pei
Technische Universiteit Eindhoven (NL)
Mykola Pechenizkiy's picture
Mykola Pechenizkiy
EEG Classification and Short-Term Epilepsy Prognosis using Brain Computer Interface Software
Abstract: 
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing.
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Konstantinos Zoulis's picture
Konstantinos Zoulis
Euripidis Glavas's picture
Euripidis Glavas
Technological Educational Institute of Epirus (GR)
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers
Abstract: 
P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.
Mina Meshriky's picture
Mina Meshriky
Ain Shams University (EG)
Seif Eldawlatly's picture
Seif Eldawlatly
Gamal Aly's picture
Gamal Aly
Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization
Abstract: 
Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used.
Prasanth Ganesan's picture
Prasanth Ganesan
Kristina Shillieto's picture
Kristina Shillieto
Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)

Keynote Speech

Vangelis Karkaletsis's picture
Vangelis Karkaletsis
NCSR Demokritos (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Conference room
Session time
Friday, June 23, 2017 - 15:30 to 16:15

Unobtrusive clinical monitoring with robots in AAL environments: the RADIO ecosystem
Technical advancements in ICT, including robotics, bring new opportunities to improve the quality of life of the elderly, their family and care-givers, and to mimimize the invonvenience and cost of clinical monitoring. Automatically detecting early symptoms of cognitive impairment, frailty and social exclusion would extend people's ability to safely and comportably live independently. 
The ecosystem proposed by the H2020 RADIO project (http://radio-project.eu/) aims to integrate multiple RADIO Home deployments, medical institutions, and informal care givers into an information management and sharing Ecosystem that is by design scalable, secure and privacy-preserving. RADIO Home is, effectively, a robot operating inside a Smart Home. In this environment, Smart Home and robot functionalities accommodate the user’s needs, while assuming interaction with the users as an opportunity for clinical monitoring. In this manner, clinical monitoring sensors do not need to be masked but become an obvious, yet discrete and accepted, part of the user’s daily life. Moreover, a robot has a dynamic presence in the user’s space, which can increase the feeling of safety by being in the right place at the right time. 
The talk will present the project outcomes so far, focusing on the clinical and unobtrusiveness requirements which guided the design of the RADIO architecture, and on the architectural and methodological work to ensure that multiple RADIO Homes and care-givers can be interconnected in a scalable, secure and privacy-preserving ecosystem. It will also present the outcomes of real-environment piloting performed in the premises of our clinical partners, to evaluate the usability of the system, its fitness for its medical purpose and the compliance with unobtrusiveness.

ST4 - Computer-aided and robotic endoscopy systems

Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Improved Barrett's Cancer Detection in Volumetric Laser Endomicroscopy scans using Multiple-Frame Voting
Abstract: 
This paper explores the feasibility of using multi-frame analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Barett's Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers considerable advantages compared to current methods.
Alexandros Rikos's picture
Alexandros Rikos
Fons van der Sommen's picture
Fons van der Sommen
Eindhoven Univeristy of Technology (NL)
Anne-Fre Swager's picture
Anne-Fre Swager
Svitlana Zinger's picture
Svitlana Zinger
Wouter Curvers's picture
Wouter Curvers
Erik Schoon's picture
Erik Schoon
Jacques Bergman's picture
Jacques Bergman
Peter de With's picture
Peter de With
Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures
Abstract: 
Here we describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to create training data: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts.
Chuanhai Zhang's picture
Chuanhai Zhang
Iowa State University (USA)
Wallapak Tavanapong's picture
Wallapak Tavanapong
Johnny Wong's picture
Johnny Wong
Piet C. de Groen's picture
Piet C. de Groen
Junghwan Oh's picture
Junghwan Oh
Sparse coded handcrafted and deep features for colon capsule video summarization
Abstract: 
Abstract—Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space.
Ahmed Mohammed's picture
Ahmed Mohammed
Norwegian University of Science and Technology (NO)
Sule Yildirim's picture
Sule Yildirim
Marius Pedersen's picture
Marius Pedersen
Oistein Hovde's picture
Oistein Hovde
Faouzi Cheikh's picture
Faouzi Cheikh
Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks
Abstract: 
Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non- invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation.
George Dimas's picture
George Dimas
Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Alexandros Karargyris's picture
Alexandros Karargyris
Gastone Ciuti's picture
Gastone Ciuti
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Retinal OCT Image Segmentation Using Fuzzy Histogram Hyperbolization and Continuous Max-Flow
Abstract: 
The segmentation of retinal layers is vital for tracking progress of medication and diagnosis of various eye diseases. To date many methods for the analysis exists, however the speckle noise and shadows of retinal blood vessel remains a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts have been focused on image pre-processing or developing sophisticated models for segmentation to address this problem, but it still remains an area of active research.
Bashir Dodo's picture
Bashir Dodo
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu

GT6: eHealth studies

Carolyn Mcgregor's picture
Carolyn Mcgregor
University of Ontario (CA)
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Comparing the Quality of Numeracy Assessment Methods in Healthcare
Abstract: 
Numeracy skill level of patients has great influence on their preferences and priorities for the treatment options concerning their healthcare. There have been different methods for assessment of numeracy skill in healthcare domain. In our previous research we proposed a new Confidence-based Patient Numeracy Assessment (C-PNA) method. In this paper we compare it with other numeracy assessment methods in terms of newly proposed quality characteristics.
Mandana Omidbakhsh's picture
Mandana Omidbakhsh
Olga Ormandjieva's picture
Olga Ormandjieva
Concordia University
Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study
Abstract: 
Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. Many medical best practice guidelines exist in the format of hospital handbooks which are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is an effective tool to model medical guidelines and enables clinical validation with medical staffs. However, some advanced statechart elements could result in high cost, such as low understandability, high difficulty in clinical validation, formal verification, and failure trace back.
Chunhui Guo's picture
Chunhui Guo
Illinois Institute of Technology (USA)
Zhicheng Fu's picture
Zhicheng Fu
Shangping Ren's picture
Shangping Ren
Yu Jiang's picture
Yu Jiang
Maryam Rahmaniheris's picture
Maryam Rahmaniheris
Lui Sha's picture
Lui Sha
Trust, Ethics and Access: Challenges in studying the work of Multidisciplinary Medical Teams
Abstract: 
This paper highlights the challenges for researchers when undertaking research on multidisciplinary medical teams (MDTs) in real-world healthcare settings, and suggests ways in which these challenges may be addressed.
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
Using Affective Computing to automatically adapt serious games for rehabilitation
Abstract: 
Although many studies investigate the automatic adaptation in serious games with the goal to improve the user's motivation, the most part of Affective Computing approaches requires a high development cost and usually does not consider an intervention of health professionals in the control of adaptations that will be executed in a game. This paper describes an approach to enable affective adaptation in serious games for motor rehabilitation with the involvement of physiotherapists.
Renan Vinicius Aranha's picture
Renan Vinicius Aranha
University of Sao Paulo (BR)
Leonardo Souza Silva's picture
Leonardo Souza Silva
Marcos Lordello Chaim's picture
Marcos Lordello Chaim
Fatima De Lourdes Dos Santos Nunes's picture
Fatima De Lourdes Dos Santos Nunes

ST3 - Immersive Personalized Patient Engagement; Computer Based Medical Intervention Systems

Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Detecting Gamification in Breast Cancer Apps: an automatic methodology for screening purposes
Abstract: 
Breast cancer is the most common cancer in women both in developed and developing countries. More than half of all cancer mobile application concern breast cancer. Gamification is widely used in mobile software applications created for health-related services. Current prevalence of gamification in breast cancer apps is unknown and detection must be manually performed. The purpose of this study is to describe and produce a tool allowing automatic detection of apps which contain gamification elements and thus empowering researchers to study gamification using large data samples.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Diego Giunta's picture
Diego Giunta
Santiago Hors-Fraile's picture
Santiago Hors-Fraile
Minna Isomursu's picture
Minna Isomursu
Diana Karoseviciute's picture
Diana Karoseviciute
Connected Health in Multiple Sclerosis: a mobile applications review
Abstract: 
Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Estefania Guisado-Fernandez's picture
Estefania Guisado-Fernandez
Brian Caulfield's picture
Brian Caulfield
A Framework for Morphological Feature Extraction of Organs from MR Images for Detection and Classification of Abnormalities
Abstract: 
In clinical practice, a misdiagnosis can lead to incorrect treatment, delayed treatment, or in some cases, no treatment at all; consequently, the condition of a patient may worsen to varying degrees, in some cases proving fatal. The accurate 3D reconstruction of organs, which is a pioneering tool of medical image computing (MIC) technology, plays a key role in computer aided diagnosis (CADx), whereby enabling medical professionals to perform enhanced analysis on a region of interest.
Barbara Villarini's picture
Barbara Villarini
University of Westminster (UK)
Hykoush Asaturyan's picture
Hykoush Asaturyan
E. Louise Thomas's picture
E. Louise Thomas
Rhys Mould's picture
Rhys Mould
Jimmy D Bell's picture
Jimmy D Bell
Visualization of Wearable Data and Biometrics for Analysis and Recommendations in Childhood Obesity
Abstract: 
Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe our experiences with the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar.
Michael Aupetit's picture
Michael Aupetit
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
The 360QS Toolkit for Sleep and Physical Activity Analysis based on Wearables
Abstract: 
Sleep and physical activity are human behaviors that play a major role in our health. Poor sleep or lack of physical activity have been found to increase health risks and reduce quality of life. The rapid adoption and evolution of pervasive computing systems, both in the health and wellness domain, are creating a new data-intensive context in which we can learn about the sleep and physical activity patterns of individuals. In this paper we provide an overview of the toolkit we have developed to conduct research on personal health data about sleep and physical activity.
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
PhytoCloud: A Gamified Mobile Web Application to Modulate Diet and Physical Activity of Women with Breast Cancer
Abstract: 
Breast cancer incidence and mortality rates vary geographically reflecting factors including regional and cultural differences in diet and lifestyle. There are numerous successful commercial mobile apps to help dieters control their diet and manage weight. However, such products are not suitable for people with special medical conditions that may require targeted dietary as well as motivational support.
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Miriam Dwek's picture
Miriam Dwek
Claire Roberston's picture
Claire Roberston
Elliott Bradley's picture
Elliott Bradley
Thanos Kounenis's picture
Thanos Kounenis
Mohammad Ramezanian's picture
Mohammad Ramezanian
Nathan Bell's picture
Nathan Bell
Tayebeh Azimi's picture
Tayebeh Azimi
Towards evidence based m-health application design in cancer patient healthy lifestyle interventions.
Abstract: 
Cancer is one of the most prevalent diseases in Europe and the world. Significant correlations between dietary habits and cancer incidence and mortality have been confirmed by the literature. Physical activity habits are also directly implicated in the incidence of cancer. Lifestyle behaviour change may be benefited by using mobile technology to deliver health behaviour interventions. M-Health offers a promising cost-efficient approach to deliver en-masse interventions.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Octavio Rivera-Romero's picture
Octavio Rivera-Romero
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Virtual scenarios for stealth assessment of the elderly; Perceptions and acceptance of technology-based health and wellness interventions.
Abstract: 
As adults get older, the risks are increasing on their health, such as chronic diseases, functional decline and geriatric syndromes which threaten their well-being. Technology has many features to support aging wellness enabling older people maintaining healthy and sociable as they grow. However, the needs of the elderly of the population are not always the same. Stealth assessment, coming from the educational domain, can assess such needs.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Anastasios Siountas's picture
Anastasios Siountas
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

ST2 - Social data and medical data analytics

Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Athena Vakali's picture
Athena Vakali
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms
Abstract: 
Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and variable over time. Typically, patients are asked to report symptoms, their severity, and duration retrospectively, e.g., in interviews or self-report questionnaires. However, little is known on how well such retrospective reports correspond with the experience of the symptoms at the moment they occurred in daily life. Mobile technologies can help in that end: mobile self-help services allow patients to record their symptoms prospectively, when (or short after) they occur in daily life.
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Thomas Probst's picture
Thomas Probst
Winfried Schlee's picture
Winfried Schlee
Johannes Schobel's picture
Johannes Schobel
Berthold Langguth's picture
Berthold Langguth
Patrick Neff's picture
Patrick Neff
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Manfred Reichert's picture
Manfred Reichert
Mining Facebook data of people with rare diseases
Abstract: 
"This research is concerned with the study of Spanish Facebook pages that deal with rare diseases. The objectives of this research are to characterise these pages and to compare them with the priorities of the Decalogue of the Spanish Federation of Rare Diseases (FEDER). This research uses Netvizz to download the data, word clouds in R to perform text mining, TextBlob in Python to perform sentiment analysis, and log-likelihood in R to compare Facebook and Decalogue words.
Natalia Reguera's picture
Natalia Reguera
Laia Subirats's picture
Laia Subirats
Eurecat & Open University of Catalonia (SP)
Manuel Armayones's picture
Manuel Armayones
OncoCall: analyzing the outcomes of the oncology telephone patient assistance
Abstract: 
Hospital Puerta del Hierro in Madrid, Spain, implemented in November 2011 a new service that aims to aid the patients of the oncology service with their doubts during their treatments through the use of a centralized call center. This service was created with the aim of provide a more personalized patient attention as well as to try to reduce the number of re-entries in the hospital in the emergencies.
Ernestina Menasalvas's picture
Ernestina Menasalvas
Consuelo Gonzalo's picture
Consuelo Gonzalo
Universidad Politécnica de Madrid (SP)
Juan Manuel Tunas's picture
Juan Manuel Tunas
Universidad Politécnica de Madrid
Alejandro Rodriguez Gonzalez's picture
Alejandro Rodriguez Gonzalez
Mariano Provencio's picture
Mariano Provencio
Cristina Gonzalez de Pedro's picture
Cristina Gonzalez de Pedro
Marta Mendez's picture
Marta Mendez
Olga Zaretskaia's picture
Olga Zaretskaia
Juan Luis Cruz's picture
Juan Luis Cruz
Jesus Rey's picture
Jesus Rey
Consuelo Parejo's picture
Consuelo Parejo
Reciprocity and Its Association with Treatment Adherence in an Online Breast Cancer Forum
Abstract: 
Online health communities (OHCs) are increasingly relied upon by individuals exchanging social support for diagnoses and treatment regimens. It has been shown that social support from close relationships (e.g., family and friends) positively influence treatment adherence in offline environments, but much less is known about the online setting.
Zhijun Yin's picture
Zhijun Yin
Vanderbilt University (USA)
Lijun Song's picture
Lijun Song
Bradley Malin's picture
Bradley Malin
Combining Subgroup Discovery and Clustering to Identify Diverse Subpopulations in Cohort Study Data
Abstract: 
Subgroup discovery (SD) exploits its full value in applications where the goal is to generate understandable models. Epidemiologists search for statistically significant relationships between risk factors and outcome in large and heterogeneous datasets encompassing information about the participants' health status gathered from questionnaires, medical examinations and image acquisition. SD algorithms can help epidemiologists by automatically detecting such relationships presented as comprehensible rules, aiming to ultimately improve prevention, diagnosis and treatment of diseases.
Uli Niemann's picture
Uli Niemann
University of Magdeburg (DE)
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Bernhard Preim's picture
Bernhard Preim
Till Ittermann's picture
Till Ittermann
Henry VOlzke's picture
Henry VOlzke
Discovering data source stability patterns in biomedical repositories based on simplicial projections from probability distribution distances
Abstract: 
The degree of homogeneity of statistical distributions among data sources is a critical issue when reusing data of Integrated Data Repositories (IDR). Evaluating this data source stability is of utmost importance in order to ensure a confident data reuse. This work tackles the task of discovering and classifying patterns among the statistical distributions of multiple sources in IDRs, by means of a novel approach based on simplicial projections from probability distribution distances, combined with Density-based spatial clustering of applications with noise (DBSCAN).
Pablo Ferri-Borredà's picture
Pablo Ferri-Borredà
Universitat Politècnica de València (SP)
Carlos Saez's picture
Carlos Saez
Juan Miguel Garcia-Gomez's picture
Juan Miguel Garcia-Gomez
Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach
Abstract: 
In Obstructive Sleep Apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May of 2015.
Daniela Ferreira-Santos's picture
Daniela Ferreira-Santos
CINTESIS (PT)
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Disease-Based Clustering of Hospital Admission: Disease Network of Hospital Networks Approach
Abstract: 
To improve the quality of healthcare planning, healthcare systems face challenges in identifying clusters of similar hospitals while considering varying factors. Clustering hospitals based on their admission behavior would be helpful whereas diagnosis of patients is vital in understanding variation in admission. Therefore, grouping hospitals that show similar behavior on their admission distribution while considering similarity among disease symptoms in admission is the objective of our study.
Nouf Albarakati's picture
Nouf Albarakati
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)

UNCAP Workshop

Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 12:30
Trilogis SRL
Abstract: 
Beyond UNCAP: A vision for the future
Aristotle University of Thessaloniki & Hololamp
Abstract: 
CAPTAIN: Coach Assistant via Projected and Tangible Interfaces
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
Nively & Trilogis
Abstract: 
MentorAge
Attikon Hospital and Medical School, University of Athens, Greece
Abstract: 
Cognitive impairment, technology and the need for respecting the individual. A global and Greek perspective
Sokratis G Papageorgiou's picture
Sokratis G Papageorgiou
Ion Beratis's picture
Ion Beratis
Attikon Hospital
LLM Care & ThessAHALL
Abstract: 
LLM Care ecosystem & Thessaloniki Active and Healthy Ageing Living Lab
Evangelia Romanopoulou's picture
Evangelia Romanopoulou
Aristotle University of Thessaloniki (GR)
Institute of Informatics and Telecommunications, NCSR "Demokritos
Abstract: 
The RADIO Ecosystem: unobtrusive clinical monitoring with robots in AAL environments
Vangelis Karkaletsis's picture
Vangelis Karkaletsis
NCSR Demokritos (GR)
Salumedia & Neurofeedback Centre of Northern Greece
Abstract: 
SmokeFreeBrain: Smoking Cessation with mobile technologies and neurofeedback
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Stathis Sidiropoulos's picture
Stathis Sidiropoulos
Aristotle University of Thessaloniki (GR)
Bioassist
Abstract: 
The heartaround platform: Providing homecare and assisted living services
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
ATOS Research & Innovation
Abstract: 
Complex Event Processing in Health Care
Aristotle University of Thessaloniki & Parkinson’s Patient Association of Northern Greece
Abstract: 
i-Prognosis: systems for early detection of Parkinson’s symptoms
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Sevasti Bostantjopoulou's picture
Sevasti Bostantjopoulou
Aristotle University of Thessaloniki
SmartCardia SA
Abstract: 
Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea
GiStandards
Abstract: 
UNCAP: Ubiquitous iNteroperable Care for Ageing People
Martin Ford's picture
Martin Ford
-
WAVES partnership and St George’s University of London
Abstract: 
WAVES: “Widening Access to Virtual Educational Scenarios for Care providers”
Sheetal Kavia's picture
Sheetal Kavia
St George's University of London (UK)

Posters II

Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 15:45
A reworked SOBI algorithm based on SCHUR Decomposition for EEG data processing
Abstract: 
In brain machine interfaces (BMI) that are used to control motor rehabilitation devices there is the need to process the monitored brain signals with the purpose of recognizing patient’s intentions to move his hands or limbs and reject artifact and noise superimposed on these signals. This kind of processing has to take place within time limits imposed by the on-line control requirements of such devices. A widely used algorithm is the Second Order Blind Identification (SOBI,) independent component analysis (ICA) algorithm.
Gregory Kalogiannis's picture
Gregory Kalogiannis
Aristotle University of Thessaloniki (GR)
Nikolaos Karampelas's picture
Nikolaos Karampelas
George Hassapis's picture
George Hassapis
Relation between fetal HRV and value of umbilical cord artery pH in labor, a study with entropy measures
Abstract: 
The relation between fetal heart rate and the value of umbilical cord artery pH is not something new for the scientific community. However, the problem has not been investigated sufficiently. One reason for that is the lack of open databases with a large number of recordings. Such a database is used here, recently publicly available, with cardiotocographic data recorded approximately two hours before delivery and until the end of the delivery. We use entropy measures to investigate how the value of umbilical cord artery pH is correlated to the variability of the fetal heart rhythm.
George Manis's picture
George Manis
University of Ioannina (GR)
Roberto Sassi's picture
Roberto Sassi
A comparison study on EEG signal processing techniques using motor imagery EEG data
Abstract: 
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work we provide a review of various existing techniques for the identification of motor imagery (MI) tasks. More specifically we perform a comparison between CSP related features and features based on Power Spectral Density (PSD) techniques.
Vangelis Oikonomou's picture
Vangelis Oikonomou
Centre for Research and Technology Hellas CERTH - ITI (GR)
Kostas Georgiadis's picture
Kostas Georgiadis
Georgios Liaros's picture
Georgios Liaros
Spiros Nikolopoulos's picture
Spiros Nikolopoulos
Centre for Research and Technology Hellas ITI-CERTH (GR)
Ioannis Kompatsiaris's picture
Ioannis Kompatsiaris
Meeting technology and methodology into Health Big Data Analytics scenarios
Abstract: 
Health organizations are collecting more data from a wider array of sources at greater speed every day. The analysis of this vast amount and array of data creates new opportunities to deliver modern personalized health and social care services. Big Data Analytics and underlying technologies have the potential to process and analyze these data to extract meaningful insights for improving quality of care, efficiency and sustainability of health and social care systems.
Pedro Gonzalez Alonso's picture
Pedro Gonzalez Alonso
Universitat Oberta de Catalunya (UOC)
Ruth Vilar's picture
Ruth Vilar
Universitat Oberta de Catalunya (SP)
Francisco Lupianez Villanueva's picture
Francisco Lupianez Villanueva
User and Stakeholder Requirements of eHealth Support Tool Viewed In a Self-Determination Theory Lens
Abstract: 
This paper presents preliminary results of an analysis of user requirements for an eHealth tool supporting chronic patients to use their personal strengths in health management. We conclude that Self-Determination Theory can be applied to view and categorize identified user requirements, and provide a framing for the analysis grounded in motivational theory. The final model will lay the foundation for our future design and implementation of gameful designs in an eHealth tool in order to enhance user engagement, motivation, and adherence.
Stian Jessen's picture
Stian Jessen
Oslo University Hospital (NO)
Jelena Mirkovic's picture
Jelena Mirkovic
Oslo University Hospital (NO)
Cornelia Ruland's picture
Cornelia Ruland
Overlap detection for a genome assembly based on genomic signal processing
Abstract: 
Although the genome sequences of most studied organisms, like human, E. coli, and others are already known, de novo genome sequencing remains popular as a majority of genomes remains unknown. Unfortunately, sequencing machines are able to read only short fragments of DNA. Therefore, one of the basic steps in reconstructing novel genomes lies in putting these pieces of DNA, called ‘reads’, together into complete genome sequences using a process known as genome assembly. Reads joining, however, requires efficient detection of their overlaps.
Robin Jugas's picture
Robin Jugas
Brno University of Technology (CZ)
Karel Sedlar's picture
Karel Sedlar
Helena Skutkova's picture
Helena Skutkova
Martin Vitek's picture
Martin Vitek
Versatile cloud collaboration services for device-transparent medical imaging teleconsultations
Abstract: 
"In this work, we present a novel web-based platform for real-time teleconsultation services on medical imaging. The introduced platform encompasses the principles of heterogeneous Workflow Management Systems (WFMSs) and the peer-to-peer paradigm to enhance collaboration among healthcare professionals.
Georgios Rassias's picture
Georgios Rassias
Christos O. Andrikos's picture
Christos O. Andrikos
Panayiotis Tsanakas's picture
Panayiotis Tsanakas
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Z-Box merging: Ultra-fast computation of Fractal Dimension and Lacunarity
Abstract: 
"In [1], a reasonably fast method of Fractal Dimension estimation was presented. However, it has a weak point: After each subdivision of the partition table, pixels that might have been quite far apart originally might end up needing to be merged. Thus, after each subdivision, the partition table needs to be re-sorted, which is a computationally expensive operation. The ideal solution would be to order the pixels of the image in such a way, that a) pixels that are in the same cube are consecutive and b) they remain in that order even after each subdivision.
Elias Aifantis's picture
Elias Aifantis
Using Prevalence Patterns to Discover Un-Mapped Flowsheet Data in an Electronic Health Record Data Warehouse
Abstract: 
We have developed a data summarization tool called Chi2notype which leverages the star schema of the Integrating Informatics from Bench to Bedside (i2b2) vendor-neutral data-warehouse platform to characterize a patient-cohort of interest. Chi2notype calculates a chi-squared statistic for every one of the tens of thousands of facts in an Electronic Medical Record System (EMR) and uses it to rank them from most over-represented in the cohort to most under-represented.
Alex Bokov's picture
Alex Bokov
UT Health San Antonio (USA)
Angela B. Bos's picture
Angela B. Bos
Laura S. Manuel's picture
Laura S. Manuel
Alfredo Tirado-Ramos's picture
Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)
Pamela Kittrell's picture
Pamela Kittrell
Carlayne Jackson's picture
Carlayne Jackson
Gail P. Olin's picture
Gail P. Olin
ACESO: Analysis of Cervical cancer: an Evidence-based treatmentS Optimization
Abstract: 
Deciding for Cervical Cancer (CxCa) treatment is not a simple task. There are several competing factors that arise from the perspective of survival, treatment, toxicity, quality of patient’s life, as well as the geographic location of the patient, which indicates access to specific healthcare resources. All of these factors play a significant role in the ultimate decision to pursue surgery, chemotherapy, and radiation therapy.
Panagiotis Katrakazas's picture
Panagiotis Katrakazas
Marilena Tarousi's picture
Marilena Tarousi
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Automatic pigment network classification using a combination of classic texture descriptors and CNN features
Abstract: 
The presence of atypical (irregular) pigment networks can be a symptom of melanoma malignum in skin lesions, thus, their proper recognition is a critical task. For object classification problems, the application of deep convolutional neural nets (CNN) receives priority attention in these days for their high recognition rate. The descriptive features found by CNNs are more hidden than the classically applied ones for texture recognition. In this paper, we investigate whether CNN features outperform the classic texture descriptors in the classification of typical/atypical pigment network.
Melinda Pap's picture
Melinda Pap
Balazs Harangi's picture
Balazs Harangi
University of Debrecen (HU)
Andras Hajdu's picture
Andras Hajdu
University of Debrecen (HU)
Anomaly detection through temporal abstractions on intensive care data: position paper
Abstract: 
A large amount of data is continuously generated in intensive health care. An analysis of these streaming data can provide important information to improve the monitoring of the health conditions of patients. The volume, velocity and complexity of these data, which come unlabeled, make their analysis a challenging task. Machine learning techniques have been successfully used for data stream mining.
Giovana Jaskulski Gelatti's picture
Giovana Jaskulski Gelatti
University of Sao Paulo (BR)
André Carlos Ponce de Leon Ferreira de_Carvalho's picture
André Carlos Ponce de Leon Ferreira de_Carvalho
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Extracting disease-phenotype relations from text with disease-phenotype concept recognisers and association rule mining
Abstract: 
Automatically extracting phenotypes (i.e., the composite of one’s observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods.
Simon Kocbek's picture
Simon Kocbek
Garvan Institute of Medical Research Sydney (AU)
Tudor Groza's picture
Tudor Groza
An Object Oriented Approach to Model Reusability
Abstract: 
Sexual contact networks for disease transmission have been used extensively with HIV and provide valuable insight into the way the disease spreads through a population. These computationally intensive models often suffer from lack of reusability which makes them expensive to create and use. We take an innovated approach to build an HIV transmission model designed for expansion and reusability, and add additional functionality for model realism.
Laura Manuel's picture
Laura Manuel
University of Texas Health Science Center (USA)
Alfredo Tirado-Ramos's picture
Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)
Manuel Castanon Puga's picture
Manuel Castanon Puga
Bringing Bayesian networks to bedside: a web-based framework
Abstract: 
Bayesian networks are one of the most intuitive statistical models for both estimation, classification and prediction of patients’ outcomes. However, the availability of inference software in clinical settings is still limited. This work presents preliminary steps towards the creation of simple web-based forms that can access a powerful Bayesian network inference engine, making the derived models usable at bedside by both the clinicians and the patients themselves.
Raphael Oliveira's picture
Raphael Oliveira
Joana Ferreira's picture
Joana Ferreira
CINTESIS - FMUP (PT)
Diogo Libanio's picture
Diogo Libanio
Claudia Camila Dias's picture
Claudia Camila Dias
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Modeling and Integrating Human Interaction Assumptions in Medical Cyber-Physical System Design
Abstract: 
For a cyber-physical system, its execution behaviors are often impacted by human interactive behaviors. However, the assumptions about a cyber-physical system’s expected human interactive behaviors are often informally documented, or even left implicit and unspecified in system design. Unfortunately, such implicit human interaction assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit human interaction assumptions.
Zhicheng Fu's picture
Zhicheng Fu
Chunhui Guo's picture
Chunhui Guo
Illinois Institute of Technology (USA)
Shangping Ren's picture
Shangping Ren
Yi-Zong Ou's picture
Yi-Zong Ou
Lui Sha's picture
Lui Sha
A Cloud based Big Data Based Online Health Analytics for Rural NICUs and PICUs in India: Opportunities and Challenges
Abstract: 
High frequency physiological data has great potential to provide new insights for many conditions patients can develop in critical care when utilized by Big Data Analytics based Clinical Decision Support Systems, such as Artemis. Artemis was deployed in NICU at SickKids Hospital in Toronto in August 2009. It employs all the potentiality of big data. Both original data together with newly generated analytics is stored to the data persistence component of Artemis. Real-time analytics is performed in the Online Analytics component.
S. Balaji's picture
S. Balaji
Meghana Bastwadkar's picture
Meghana Bastwadkar
Carolyn Mcgregor's picture
Carolyn Mcgregor
University of Ontario (CA)
Visual Scanning Behaviour during a visual search task: an objective indicator for white matter integrity in patients with post-concussion syndrome
Abstract: 
"Post-concussion syndrome (PCS) is associated with incomplete recovery following a mild traumatic brain injury (mTBI). Often, PCS is characterised by microstructural damage to white matter tracts in the brain. Currently, there is no biomarker to screen for such damage. In this paper we present preliminary result for a novel and simple to administer paradigm that can be used to test the microstructural integrity of the corpus callosum. The novel paradigm is based on the Matching Familiar Figures Test (MFFT).
Jonathan Chung's picture
Jonathan Chung
Foad Taghdiri's picture
Foad Taghdiri
Samantha Irwin's picture
Samantha Irwin
Namita Multani's picture
Namita Multani
Apameh Tarazi's picture
Apameh Tarazi
Ahmed Ebraheem's picture
Ahmed Ebraheem
Mozhgan Khodadadi's picture
Mozhgan Khodadadi
Ruma Goswami's picture
Ruma Goswami
Richard Wennberg's picture
Richard Wennberg
Robin Green's picture
Robin Green
David Mikulis's picture
David Mikulis
Karen Davis's picture
Karen Davis
Charles Tator's picture
Charles Tator
Carmela Tartaglia's picture
Carmela Tartaglia
Moshe Eizenman's picture
Moshe Eizenman
University of Toronto (CA)
Level set based on brain radiological densities for stroke segmentation in CT images
Abstract: 
Cardiovascular diseases (CVD) are the leading cause of death worldwide, and every year more people die of these diseases. Aiming to assist medical diagnoses through Computerized Tomography (CT) scans, this work proposes a new approach to segment CT images of the brain damaged by stroke. The proposed method takes into account two improvements of the level set method based on the likelihood of Normal distribution.
Elizangela De Souza Rebouças's picture
Elizangela De Souza Rebouças
Alan M. Braga's picture
Alan M. Braga
Roger Moura Sarmento's picture
Roger Moura Sarmento
Segmentation and visualization of the lungs in three dimensions using 3D Region Growing and Visualization Toolkit in CT examinations of the chest
Abstract: 
Computed tomography (CT) stands out among the exams used by computer-aided diagnosis in medical imaging as it provides the visualization of internal organs such as the lungs and their structures. This paper focuses on the segmentation of lungs using a three-dimensional region growing (3D RG) method and the registration toolkit ITK library. To evaluate the proposed segmentation method, we used 30 exams from the LAPISCO image database.
Raul Victor Medeiros Nobrega's picture
Raul Victor Medeiros Nobrega
Murillo Barata Rodrigues's picture
Murillo Barata Rodrigues
Application of affinity analysis techniques on diagnosis and prescription data
Abstract: 
"This study performs an Affinity Analysis on diagnosis and prescription data in order to discover co-occurrence relationships among diagnosis and pharmaceutical active ingredients prescribed to different patient groups. The analysis data collected during consecutive visits of 4,473 patients in a 3 years period, focused on patients suffering by hypertension and/or hypercholesterolemia and applied association rule and sequential rule mining techniques.
Theodora Sanida's picture
Theodora Sanida
Harokopio University of Athens (GR)
Iraklis Varlamis's picture
Iraklis Varlamis
An Automatic EEG Based System For The Recognition of Math Anxiety
Abstract: 
Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematically rich situations. Although MA seems innocent in general, it can seriously compromise math performance, lead to avoidance, affect the learning procedure, as well as influence future career choices and directions. The accurate recognition of MA, apart from diagnostic purposes, is considered to be very important for e-learning systems as well. This work presents an automatic system for the detection of MA based on electroencephalographic (EEG) signals.
Manousos A. Klados's picture
Manousos A. Klados
Aston University (UK)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings
Abstract: 
This paper focuses on developing a novel approach to automatic sleep stage classification based on electroencephalographic (EEG) data. The proposed methodology employs contemporary mathematical tools such as the synchronization likelihood and graph theory metrics applied on sleep EEG data. The derived features are then fitted into three different machine learning techniques, namely k-nearest neighbors, support vector machines and neural networks. The evaluation of their comparative performance is investigated according to their accuracy.
Panteleimon Chriskos's picture
Panteleimon Chriskos
Dimitra Kaitalidou's picture
Dimitra Kaitalidou
Georgios Karakasis's picture
Georgios Karakasis
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Polyxeni Gkivogkli's picture
Polyxeni Gkivogkli
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Chrysoula Kourtidou-Papadeli's picture
Chrysoula Kourtidou-Papadeli
Visual Versus Kinesthetic Motor Imagery For BCI Control Of Robotic Arms (Mercury 2.0)
Abstract: 
Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made.
George Arfaras's picture
George Arfaras
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Kyriaki Rafailia Kavazidi's picture
Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Visual Role in the Dynamic Postural Balance due to Aging – Assessment by using an Augmented Reality Purterbation System
Abstract: 
Sensorimotor modulation is degenerated due to aging, and it may cause the elderly falling in a daily activity. To evaluate the postural balance ability, the clinical functional balance assessments, somatosensory test, muscle strength and the joint motion measurement are usually applied to evaluate the falling potential in the aged population. However, the ceiling effect is found among the sub-healthy elderly with high functional mobility but has a potential risk of falling.
Chun-Ju Chang's picture
Chun-Ju Chang
Sai-Wei Yang's picture
Sai-Wei Yang
National Yang-Ming University (TW)
Jen-Suh Chern's picture
Jen-Suh Chern
Tsui-Fen Yang's picture
Tsui-Fen Yang
Are elderly less responsive to emotional stimuli? An EEG-based study across pleasant, unpleasant and neutral Greek words
Abstract: 
A plethora of studies has shown that working memory, processing speed and flowing intelligence are diminished with aging. However, emotional processing remains relatively stable even though young and old seem to process emotions differently. Neurophysiological studies have employed emotional stimuli to investigate age differences through Event Related Potentials (ERPs). The present approach used affective visual word stimuli derived from the Greek language. Healthy young and elderly volunteers passively viewed the stimuli which were divided into pleasant, unpleasant and neutral.
Ioanna Tepelena's picture
Ioanna Tepelena
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Vasiliki Salvari's picture
Vasiliki Salvari
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Rehabilitation Biofeedback Using EMG Signal Based on Android Platform
Abstract: 
The aim of this paper is to introduce an integrated system for accurate and easy rehabilitation process. A small gadget and an installed android application for on line follow up and continuous enhancement with affordable cost. The gadget consists of instrumentation amplifier, filtration process and rectifier. The gadget is communicating with android application via Bluetooth device which send the signal to patient application. Acquiring and signal processing is implemented by application for both patient and doctor.
Mazen Yassin's picture
Mazen Yassin
Minia university (EG)
Hussein Abdallah's picture
Hussein Abdallah
Amr Anwer's picture
Amr Anwer
Abubakr Mustafa's picture
Abubakr Mustafa
Ashraf Mahroos's picture
Ashraf Mahroos
Evaluating the AffectLecture mobile app within an elementary school class teaching process
Abstract: 
Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intristic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students’ emotional status, and how it affects their academic performance.
Styliani Siouli's picture
Styliani Siouli
Aristotle University of Thessaloniki (GR)
Ioanna Dratsiou's picture
Ioanna Dratsiou
Aristotle University of Thessaloniki (GR)
Meni Tsitouridou's picture
Meni Tsitouridou
Aristotle University of Thessaloniki (GR)
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Assessing Emotional Impact of Biofeedback and Neurofeedback Training in Smokers during a Smoking Cessation Project
Abstract: 
This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed by 20 sessions of neurofeedback. Both pre- and post- biofeedback and neurofeedback training subjective scores of the participant’s mood were collected through the application.
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Conditional Entropy Based Retrieval Model in Patient-Carer Conversational Cases
Abstract: 
Assistant Robots can be an efficient and low-cost solution to Patient-Care. One important aspect of Assistant Bots is succesful as well as Socially Intelligent Communication with the Patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The Conditional Entropy Model and the Attitude Model are combined in order to record Attitude Changes in Dialogue Interactions between Patients and Doctors.
Antonis Billis's picture
Antonis Billis
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Ioannis Antoniou's picture
Ioannis Antoniou
Nikolaos Hasanagas's picture
Nikolaos Hasanagas
Charalampos Bratsas's picture
Charalampos Bratsas
Open Knowledge Greece (GR)
Provisioning and Delivering Sepsis Data Supported by an Enhanced SDN Environment
Abstract: 
"Medical applications, along with Information and Communication Technology (ICT), have contributed with many solutions to supporting the treatment of SEPSIS. However, there are few solutions for the transport of sepsis data with QoS (Quality of Service). Using SDN (Software-Defined Networking), in this paper we propose a self-manageable architecture for the provision and delivery of sepsis data. To evaluate our proposal, we conducted our experiments in the laboratory.
Felipe Volpato's picture
Felipe Volpato
Universidade Federal de Santa Catarina (BR)
Madalena Pereira Da Silva's picture
Madalena Pereira Da Silva
Alexandre Leopoldo Gonçalves's picture
Alexandre Leopoldo Gonçalves
Marcio Castro's picture
Marcio Castro
Mario A. R. Dantas's picture
Mario A. R. Dantas
Memorandum: An Android App for Efficient Note Keeping in Concurrent Multi-Participant Human Subject Studies
Abstract: 
Note keeping is an indispensable ingredient of successful research. Although traditionally performed on paper, recently the task is increasingly facilitated by Electronic Lab Notebooks, i.e., ICT programs that allow their users to make electronic observation in laboratory contexts. Owing to recent advances in mobile technologies, Smartphones and Tablets have emerged as promising alternatives for electronic note keeping.
Leandros Stefanopoulos's picture
Leandros Stefanopoulos
Aristotle University Thessaloniki (GR)
Christos Maramis's picture
Christos Maramis
Ioannis Moulos's picture
Ioannis Moulos
Ioannis Ioakimidis's picture
Ioannis Ioakimidis
Nicos Maglaveras's picture
Nicos Maglaveras
Methods for enhancing the reproducibility of observational research using electronic health records: preliminary findings from the CALIBER resource
Abstract: 
The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of health research by the wider community. However, a substantial proportion of health research using electronic health records, data collected and generated during routine clinical care, cannot be reproduced. With the complexity, volume and variety of electronic health records made available for research steadily increasing, it is critical to ensure that findings from such data are reproducible and replicable by researchers.
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Arturo Gonzalez-Izquierdo's picture
Arturo Gonzalez-Izquierdo
Maria Pikoula's picture
Maria Pikoula
Kenan Direk's picture
Kenan Direk
Natalie Fitzpatrick's picture
Natalie Fitzpatrick
Harry Hemingway's picture
Harry Hemingway
Liam Smeeth's picture
Liam Smeeth
Evaluating openEHR for storing computable representations of electronic health record-driven phenotyping algorithms
Abstract: 
Electronic Health Records (EHR) are structured and unstructured data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of translation and enable precision medicine at scale. One of the main use-cases of EHR is creating algorithms to define disease status, onset and severity from diagnoses, prescriptions, laboratory tests, symptoms and other EHR elements - a process known as phenotyping.
Vaclav Papez's picture
Vaclav Papez
University College London (UK)
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Probabilistic integration of large Brazilian socioeconomic and clinical databases
Abstract: 
The integration of disparate large and heterogeneous socioeconomic and clinical databases is now considered essential to capture and model longitudinal and social aspects of diseases. However, such integration has significant challenges associated with it. Databases are often stored in disparate locations, make use of different identifiers, have variable data quality, record information in bespoke purpose-specific formats and have different levels of associated metadata.
Marcos Barreto's picture
Marcos Barreto
University College London (UK)
Clicia Pinto's picture
Clicia Pinto
Robespierre Pita's picture
Robespierre Pita
George Barbosa's picture
George Barbosa
Samila Sena's picture
Samila Sena
Rosemeire Fiaccone's picture
Rosemeire Fiaccone
Leila D. A. F. Amorim's picture
Leila D. A. F. Amorim
Maria Yuri Ichihara's picture
Maria Yuri Ichihara
Mauricio Barreto's picture
Mauricio Barreto
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Sandra Reis's picture
Sandra Reis
Bruno Araujo's picture
Bruno Araujo
Juracy Bertoldo's picture
Juracy Bertoldo
A Three-phase Motor Relearning: error-potentials, motor correction and neurofeedback for memory-consolidation
Abstract: 
Three components are embedded in relearning of motor skills: error detection, training to reduce errors, and memory consolidation of the improved motor execution. We integrate here three components that we previously reported to suggest a three stage paradigm for error detection via EEG, correction through BCI and memory consolidation of the corrected motor motion through neurofeedback memory consolidation. In our earlier work we that the characteristics of the error potential is associated with the type of error, rather than being generic, by that providing a BCI system for error correction.
Miriam Reiner's picture
Miriam Reiner
Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome using a Naïve Bayesian Tree Classifier
Abstract: 
Successful post-stroke prognosis and recovery strategies are heavily dependent on our understanding about how the damage to one specific region may impact to other remote regions, as well as the various functional networks involved in efficient cognitive function. In this study, 27 consecutive ischemic stroke patients were recruited. Stroke patients underwent two complete neuropsychological assessments between the first 72 hours after stroke arrival and three months later. They were further evaluated with a MRI protocol at 3 months.
Rosalia Dacosta-Aguayo's picture
Rosalia Dacosta-Aguayo
Christian Stephan-Otto's picture
Christian Stephan-Otto
Tibor Auer's picture
Tibor Auer
Ic Clemente's picture
Ic Clemente
Antoni Davalos's picture
Antoni Davalos
Nuria Bargallo's picture
Nuria Bargallo
Maria Mataro's picture
Maria Mataro
Manousos A. Klados's picture
Manousos A. Klados
Aston University (UK)

GT8: Ask the Editor

Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Conference room
Session time
Saturday, June 24, 2017 - 11:30 to 12:30

Editors-In-Chief of three PubMed journals publishing research on Healthcare Technology present their journals' interests. The audience may interact to obtain guidelines and best recipies for papers to be prepared for acceptance!

 

  • Dimitris Fotiadis
  • Luis Kun
  • Stefanos Triaridis

GT7 Medical Imaging I

Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Conference room
Session time
Saturday, June 24, 2017 - 11:30 to 12:30
Illumination correction by dehazing for retinal vessel segmentation
Abstract: 
"Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation.
Benedetta Savelli's picture
Benedetta Savelli
Alessandro Bria's picture
Alessandro Bria
Claudio Marrocco's picture
Claudio Marrocco
Mario Molinara's picture
Mario Molinara
Francesco Tortorella's picture
Francesco Tortorella
University of Cassino and South Lazio (IT)
Adrian Galdran's picture
Adrian Galdran
Aurelio Campilho's picture
Aurelio Campilho
A differential geometry approach for change detection in medical images.
Abstract: 
Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering.
Alexander Naitsat's picture
Alexander Naitsat
Technion – Israel Institute of Technology (IL)
Emil Saucan's picture
Emil Saucan
Yehoshua Zeevi's picture
Yehoshua Zeevi
Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images
Abstract: 
During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease.
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Christos Papacharalambous's picture
Christos Papacharalambous
Giorgos Samaras's picture
Giorgos Samaras
Efthivoulos Kyriakou's picture
Efthivoulos Kyriakou
Frederick University (CY)
Takis Kasparis's picture
Takis Kasparis
Cyprus University of Technology (CY)
Marios Pantziaris's picture
Marios Pantziaris
Eleni Eracleous's picture
Eleni Eracleous
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
An Eye Tracker–based Computer System to Support Oculomotor and Attention Deficit Investigations
Abstract: 
Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation.
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Carmelo Pino's picture
Carmelo Pino
Isaak Kavasidis's picture
Isaak Kavasidis
Concetto Spampinato's picture
Concetto Spampinato
Massimo Di Pietro's picture
Massimo Di Pietro
Renata Rizzo's picture
Renata Rizzo
Anna Scuderi's picture
Anna Scuderi
Rita Barone's picture
Rita Barone

Keynote Speech

Luis Kun's picture
Luis Kun
Editor in Chief - Springer (USA)
Conference room
Session time
Saturday, June 24, 2017 - 12:30 to 13:15

Most medical treatments are designed for a specific disease and for the “average patient".  This "one-size-fits-all-approach," may be successful for some patients but not for others. Precision medicine, sometimes known as "personalized medicine" is an innovative approach to disease prevention and treatment that takes into account differences in people’s genes, environments and lifestyles.  In January 2015, President Obama launched the Precision Medicine Initiative (PMI), a bold new research effort to revolutionize how we improve health and treat disease, empowering health care providers to tailor treatment and prevention strategies to individuals’ unique characteristics.  Advances in precision medicine have already led to powerful new discoveries and several new FDA-approved treatments that are tailored to specific characteristics of individuals, such as a person’s genetic makeup, or the genetic profile of an individual’s tumor. Patients with a variety of cancers routinely undergo molecular testing as part of patient care, enabling physicians to select treatments that improve chances of survival and reduce exposure to adverse effects.  The PMI seeks to identify genetically-based drivers of disease in order to develop new, more effective treatments.  There is a component in this equation that relates to Public Health that is very seldom discussed or incorporated into an individual’s health record: Geomedicine.  This presentation will discuss how this field will change the future model of Healthcare.

GT9: Games Robotics and smart technologies

Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
On supporting Parkinson's Disease patients: The i-PROGNOSIS Personalized Game Suite design approach
Abstract: 
The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games).
S. B. Dias's picture
S. B. Dias
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
J. A. Diniz's picture
J. A. Diniz
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
V Charisis's picture
V Charisis
S Hadjidimitriou's picture
S Hadjidimitriou
M. Stadtschnitzer's picture
M. Stadtschnitzer
P. Fagerberg's picture
P. Fagerberg
Ioannis Ioakimidis's picture
Ioannis Ioakimidis
K Dimitropoulos's picture
K Dimitropoulos
N Grammalidis's picture
N Grammalidis
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Carotid Bifurcation Plaque Stability Estimation based on Motion Analysis
Abstract: 
"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systoly and diastoly sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farneback’s optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval.
Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Andrew Nicolaides's picture
Andrew Nicolaides
Alexandra Constantinou's picture
Alexandra Constantinou
Maura Griffin's picture
Maura Griffin
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Marios S. Pattichis's picture
Marios S. Pattichis
Hamed Nasrabadi's picture
Hamed Nasrabadi
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
A Versatile Architecture for Building IoT Quantified-Self Applications
Abstract: 
The abundance of activity trackers and biosignal sensors as well as the evolution of IoT and communication technologies have considerably advanced the concept of Quantified-Self. Nowadays there are several frameworks and applications that realize the concept, focusing though strictly on specific areas, from daily use to professional activities such as sport and healthcare. This work proposes a versatile, cross-domain solution for building quantified-self applications exploiting the capacities for open-design, modularity and extensibility of the AGILE IoT gateway.
Charalampos Doukas's picture
Charalampos Doukas
Panayiotis Tsanakas's picture
Panayiotis Tsanakas
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Commercial BCI Control And Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept.
Abstract: 
Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms.
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
George Arfaras's picture
George Arfaras
Ioannis Xygonakis's picture
Ioannis Xygonakis
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Kyriaki Rafailia Kavazidi's picture
Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Nicolas Foroglou's picture
Nicolas Foroglou
Konstantinos Polyzoidis's picture
Konstantinos Polyzoidis
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT10 Medical Imaging II

Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images
Abstract: 
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.
Harshita Sharma's picture
Harshita Sharma
Technical University Berlin (DE)
Norman Zerbe's picture
Norman Zerbe
Christine BOger's picture
Christine BOger
Stephan Wienert's picture
Stephan Wienert
Olaf Hellwich's picture
Olaf Hellwich
Peter Hufnagl's picture
Peter Hufnagl
Non-Invasive Assessment of Coronary Stenoses and Comparison to Invasive Techniques: a proof-of-concept study
Abstract: 
Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques.
Panagiota Tsompou's picture
Panagiota Tsompou
Panagiotis Siogkas's picture
Panagiotis Siogkas
University of Ioannina (GR)
Antonis Sakellarios's picture
Antonis Sakellarios
Pedro Lemos's picture
Pedro Lemos
Lampros Michalis's picture
Lampros Michalis
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm
Abstract: 
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.
Dimosthenis C. Tsouros's picture
Dimosthenis C. Tsouros
Panagiotis N. Smyrlis's picture
Panagiotis N. Smyrlis
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Pinelopi Manousou's picture
Pinelopi Manousou
Dimitrios G. Tsalikakis's picture
Dimitrios G. Tsalikakis
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
A Deconstructed Replication of Time of Test Using the AGIS Metric (Skyline paper)
Abstract: 
In medical practice, glaucoma severity is usually measured using the Advanced Glaucoma Intervention Studies (AGIS) metric. In a previous study, we replicated the work of Montolio et al.,and demonstrated that, for a larger dataset, time of day of test using the AGIS metric did make a difference to the measurement of glaucoma, supporting Montolio et al’s work. However, in our earlier study, we used the AGIS scores for both eyes combined. In this paper, we use the measurement from just one eye at a time.
Steve Counsell's picture
Steve Counsell
Stephen Swift's picture
Stephen Swift
Brunel University (UK)
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)