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
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
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
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
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
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
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
"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
"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
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
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
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
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
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
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
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
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
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
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
"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
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
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
"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
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
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)
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
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
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
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
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
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
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
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
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
"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
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
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
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
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
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
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)