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

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)

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