IEEE CBMS 2017 - Amphitheater II

GT8: Ask the Editor

teosavv — Fri, 19 May 2017 13:10:56 +0000

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

Saturday, June 24, 2017 - 11:30 to 12:30

Amphitheater II

Pedro Pereira Rodrigues
University of Porto (PT)

GT10 Medical Imaging II

teosavv — Tue, 16 May 2017 14:20:08 +0000

Saturday, June 24, 2017 - 14:45 to 15:45

Amphitheater II

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Ilias Maglogiannis
University of Piraeus (GR)

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
Technical University Berlin (DE)

Norman Zerbe

Christine BOger

Stephan Wienert

Olaf Hellwich

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

Panagiotis Siogkas
University of Ioannina (GR)

Antonis Sakellarios

Pedro Lemos

Lampros Michalis

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

Panagiotis N. Smyrlis

Nikolaos Giannakeas

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Pinelopi Manousou

Dimitrios G. Tsalikakis

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

Stephen Swift
Brunel University (UK)

Allan Tucker
Brunel University London (UK)

Lunch

teosavv — Tue, 16 May 2017 14:10:30 +0000

Saturday, June 24, 2017 - 13:15 to 14:00

Amphitheater I

Amphitheater II

ST2 - Social data and medical data analytics

teosavv — Tue, 16 May 2017 14:06:18 +0000

Saturday, June 24, 2017 - 09:00 to 11:00

Amphitheater II

Pedro Pereira Rodrigues
University of Porto (PT)

Athena Vakali
Aristotle University of Thessaloniki (GR)

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
Ulm University (DE)

Thomas Probst

Winfried Schlee

Johannes Schobel

Berthold Langguth

Patrick Neff

Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)

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

Laia Subirats
Eurecat & Open University of Catalonia (SP)

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

Consuelo Gonzalo
Universidad Politécnica de Madrid (SP)

Juan Manuel Tunas
Universidad Politécnica de Madrid

Alejandro Rodriguez Gonzalez

Mariano Provencio

Cristina Gonzalez de Pedro

Marta Mendez

Olga Zaretskaia

Juan Luis Cruz

Jesus Rey

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
Vanderbilt University (USA)

Lijun Song

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
University of Magdeburg (DE)

Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)

Bernhard Preim

Till Ittermann

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
Universitat Politècnica de València (SP)

Carlos Saez

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
CINTESIS (PT)

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

Zoran Obradovic
Temple University (USA)

GT6: eHealth studies

teosavv — Tue, 16 May 2017 13:56:44 +0000

Friday, June 23, 2017 - 16:45 to 18:00

Amphitheater II

Carolyn Mcgregor
University of Ontario (CA)

Bridget Kane
Karlstad University Business School (SE)

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

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
Illinois Institute of Technology (USA)

Zhicheng Fu

Shangping Ren

Yu Jiang

Maryam Rahmaniheris

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
Karlstad University Business School (SE)

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
University of Sao Paulo (BR)

Leonardo Souza Silva

Marcos Lordello Chaim

Fatima De Lourdes Dos Santos Nunes

GT5 Biomedical Signal

teosavv — Tue, 16 May 2017 13:50:24 +0000

Friday, June 23, 2017 - 14:00 to 15:30

Amphitheater II

Behnaz Ghoraani
Florida Atlantic University (USA)

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

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
Aristotle University of Thessaloniki (GR)

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

Ana Vivas

Maria Karagianni

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
National Technical University of Athens (GR)

Vasileios C. Pezoulas

Dimitrios Koutsouris
National Technical University of Athens (GR)

Dimitris Fotiadis
University of Ioannina (GR)

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

Yulong Pei
Technische Universiteit Eindhoven (NL)

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
Technological Educational Institute of Epirus (GR)

Nikolaos Giannakeas

Konstantinos Zoulis

Markos Tsipouras

Euripidis Glavas
Technological Educational Institute of Epirus (GR)

Katerina D. Tzimourta

Loukas G. Astrakas

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
Ain Shams University (EG)

Seif Eldawlatly

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

Kristina Shillieto

Behnaz Ghoraani
Florida Atlantic University (USA)

Lunch

teosavv — Tue, 16 May 2017 13:47:58 +0000

Friday, June 23, 2017 - 13:15 to 14:00

Amphitheater I

Amphitheater II

Medical Curriculum Innovations: From theory to practice

teosavv — Tue, 16 May 2017 13:40:57 +0000

Friday, June 23, 2017 - 11:00 to 12:30

Amphitheater II

Nicolaos Dombros
Aristotle University of Thessaloniki (GR)

Curriculum mapping: motivation, goals and challenges

Luke Woodham
St George's University of London (UK)

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
Masaryk University (CZ)

Matej Karolyi
Masaryk University (CZ)

Christos Vaitsis

dspachos
Aristotle University of Thessaloniki (GR)

Luke Woodham
St George's University of London (UK)

MEDCIN platform: How to explore medical and healthcare curricula

Matej Karolyi
Masaryk University (CZ)

Martin Komenda
Masaryk University (CZ)

Medical Curriculum Innovations: From theory to practice

teosavv — Tue, 16 May 2017 13:36:58 +0000

Friday, June 23, 2017 - 09:00 to 10:30

Amphitheater II

Nicolaos Dombros
Aristotle University of Thessaloniki (GR)

Medcin Opening

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Cognitive Computing meets Medical Education: Smarter Educational Data

Nabil Zary
Nanyang Technological University (SG) KI (SE)

Building healthcare workforce curricula based on the crowd: The CAMEI approach

Stathis Th. Konstantinidis
University of Nottingham (UK)

Updating the Medical Curriculum at AUTH: principles and lessons learned so far

Dimitrios Goulis
Aristotle University of Thessaloniki (GR)

MEDCIN project overview

Martin Komenda
Masaryk University (CZ)

Reception

teosavv — Tue, 16 May 2017 11:11:48 +0000

Thursday, June 22, 2017 - 19:00 to 20:30

Amphitheater I

Amphitheater II