IEEE CBMS 2017 - Foyer

UNCAP Workshop

teosavv — Fri, 09 Jun 2017 14:52:44 +0000

Saturday, June 24, 2017 - 09:00 to 12:30

Foyer

Trilogis SRL

Abstract:

Beyond UNCAP: A vision for the future

Giuseppe Conti
-

Aristotle University of Thessaloniki & Hololamp

Abstract:

CAPTAIN: Coach Assistant via Projected and Tangible Interfaces

Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)

Nively & Trilogis

Abstract:

MentorAge

Giuseppe Conti
-

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

Ion Beratis
Attikon Hospital

LLM Care & ThessAHALL

Abstract:

LLM Care ecosystem & Thessaloniki Active and Healthy Ageing Living Lab

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
NCSR Demokritos (GR)

Salumedia & Neurofeedback Centre of Northern Greece

Abstract:

SmokeFreeBrain: Smoking Cessation with mobile technologies and neurofeedback

Luis Fernandez-Luque
Qatar Computing Research Institute (QA)

Stathis Sidiropoulos
Aristotle University of Thessaloniki (GR)

Bioassist

Abstract:

The heartaround platform: Providing homecare and assisted living services

Ilias Maglogiannis
University of Piraeus (GR)

Andreas Menychtas

ATOS Research & Innovation

Abstract:

Complex Event Processing in Health Care

Rodriguez Fuentes Miguel
-

Aristotle University of Thessaloniki & Parkinson’s Patient Association of Northern Greece

Abstract:

i-Prognosis: systems for early detection of Parkinson’s symptoms

Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)

Sevasti Bostantjopoulou
Aristotle University of Thessaloniki

SmartCardia SA

Abstract:

Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea

Srinivasan Murali
-

Greek Association of Alzheimers Disease & Related Disorders

Abstract:

AD-Gaming

Magda Tsolaki
-

Marina Makri
-

GiStandards

Abstract:

UNCAP: Ubiquitous iNteroperable Care for Ageing People

Martin Ford
-

WAVES partnership and St George’s University of London

Abstract:

WAVES: “Widening Access to Virtual Educational Scenarios for Care providers”

Sheetal Kavia
St George's University of London (UK)

Posters II

teosavv — Tue, 16 May 2017 14:06:47 +0000

Saturday, June 24, 2017 - 09:00 to 15:45

Foyer

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
Aristotle University of Thessaloniki (GR)

Nikolaos Karampelas

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
University of Ioannina (GR)

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
Centre for Research and Technology Hellas CERTH - ITI (GR)

Kostas Georgiadis

Georgios Liaros

Spiros Nikolopoulos
Centre for Research and Technology Hellas ITI-CERTH (GR)

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
Universitat Oberta de Catalunya (UOC)

Ruth Vilar
Universitat Oberta de Catalunya (SP)

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
Oslo University Hospital (NO)

Jelena Mirkovic
Oslo University Hospital (NO)

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
Brno University of Technology (CZ)

Karel Sedlar

Helena Skutkova

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

Christos O. Andrikos

Panayiotis Tsanakas

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.

John Nikolaides
AUTH

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
UT Health San Antonio (USA)

Angela B. Bos

Laura S. Manuel

Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)

Pamela Kittrell

Carlayne Jackson

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

Marilena Tarousi
National Technical University of Athens (GR)

Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)

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

Balazs Harangi
University of Debrecen (HU)

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
University of Sao Paulo (BR)

André Carlos Ponce de Leon Ferreira de_Carvalho

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
Garvan Institute of Medical Research Sydney (AU)

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
University of Texas Health Science Center (USA)

Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)

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

Joana Ferreira
CINTESIS - FMUP (PT)

Diogo Libanio

Claudia Camila Dias

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

Chunhui Guo
Illinois Institute of Technology (USA)

Shangping Ren

Yi-Zong Ou

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

Meghana Bastwadkar

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

Foad Taghdiri

Samantha Irwin

Namita Multani

Apameh Tarazi

Ahmed Ebraheem

Mozhgan Khodadadi

Ruma Goswami

Richard Wennberg

Robin Green

David Mikulis

Karen Davis

Charles Tator

Carmela Tartaglia

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

Alan M. Braga

Roger Moura Sarmento

Regis C. P. Marques
-

Pedro Pedrosa Rebouças Filho
IFCE (BR)

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

Murillo Barata Rodrigues

Pedro Pedrosa Rebouças Filho
IFCE (BR)

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
Harokopio University of Athens (GR)

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
Aston University (UK)

Niki Pandria
Aristotle University of Thessaloniki (GR)

Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)

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

Dimitra Kaitalidou

Georgios Karakasis

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

Polyxeni Gkivogkli
Aristotle University of Thessaloniki (GR)

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

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

Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)

Niki Pandria
Aristotle University of Thessaloniki (GR)

Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki

Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)

Alexander Astaras
American College of Thessaloniki (GR)

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

Sai-Wei Yang
National Yang-Ming University (TW)

Jen-Suh Chern

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
Aristotle University of Thessaloniki (GR)

Christos Frantzidis
Aristotle University of Thessaloniki (GR)

Vasiliki Salvari

Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)

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
Minia university (EG)

Hussein Abdallah

Amr Anwer

Abubakr Mustafa

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
Aristotle University of Thessaloniki (GR)

Ioanna Dratsiou
Aristotle University of Thessaloniki (GR)

Meni Tsitouridou
Aristotle University of Thessaloniki (GR)

Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)

dspachos
Aristotle University of Thessaloniki (GR)

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
Aristotle University of Thessaloniki (GR)

dspachos
Aristotle University of Thessaloniki (GR)

Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)

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.

Meropi Pavlidou

Antonis Billis
Aristotle University of Thessaloniki (GR)

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Ioannis Antoniou

Nikolaos Hasanagas

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
Universidade Federal de Santa Catarina (BR)

Madalena Pereira Da Silva

Alexandre Leopoldo Gonçalves

Marcio Castro

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
Aristotle University Thessaloniki (GR)

Christos Maramis

Ioannis Moulos

Ioannis Ioakimidis

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
University College London (UK)

Arturo Gonzalez-Izquierdo

Maria Pikoula

Kenan Direk

Natalie Fitzpatrick

Harry Hemingway

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
University College London (UK)

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
University College London (UK)

Clicia Pinto

Robespierre Pita

George Barbosa

Samila Sena

Rosemeire Fiaccone

Leila D. A. F. Amorim

Maria Yuri Ichihara

Mauricio Barreto

Spiros Denaxas
University College London (UK)

Sandra Reis

Bruno Araujo

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

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

Christian Stephan-Otto

Tibor Auer

Ic Clemente

Antoni Davalos

Nuria Bargallo

Maria Mataro

Manousos A. Klados
Aston University (UK)

Exhibition & Posters I

teosavv — Tue, 16 May 2017 13:39:34 +0000

Friday, June 23, 2017 - 09:00 to 18:45

Foyer

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
The University of Melbourne (AU)

Sudanthi Wijewickrema
University of Melbourne (AU)

Yun Zhou

Bridget Copson

James Bailey

Gregor Kennedy

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

Rafael Staiger Bressan
Federal University of Technology Paraná

Pedro Henrique Bugatti

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
VSB-TU Ostrava (CZ)

Martin Augustynek

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
University of Coimbra (PT)

Carlos Leal

Luis Pinto

Pedro Figueiredo

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

Markos G. Tsipouras
University of Western Macedonia

Nikolaos Giannakeas

Loukas G. Astrakas

Spyridon Konitsiotis

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

Markos Tsipouras

Alexandros Tzallas
Technological Educational Institute of Epirus (GR)

Roberta Forlano

Maria Vavva

Evaggelos C. Karvounis

Maria Tsimplakidou

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
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
Khalifa University of Science and Technology (AE)

Lakmal Seneviratne

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
National Institute of Public Health (JP)

Tomoko Kajiyama

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
Federal University of ABC

Marcelo Z. Do Nascimento

Paulo R. Faria

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
University of Debrecen (HU)

Andras Hajdu
University of Debrecen (HU)

Rudolf Lampe

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

Rudiger Pryss
Ulm University (DE)

Marc Schickler

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
University of Hagen (DE)

Bettina Sader

Christian Eichhorn

Gabriele Kern-Isberner

Ralf Georg Meyer

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

Rudiger Pryss
Ulm University (DE)

Michael Stach

Johannes Schobel

Winfried Schlee

Thomas Probst

Berthold Langguth

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
IHNA (RU)

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

Haridimos Kondylakis
Institute of Computer Science FORTH (GR)

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

Iordanis Mourouzis

George Lambrou
National Technical University of Athens (GR)

Constantinos Pantos

Dimitra Iliopoulou

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
Federal Rural University of Pernambuco (BR)

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
Federal Rural University of Pernambuco (BR)

Kallebe F. P. Bezerra

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

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
Ulm University (DE)

Rudiger Pryss
Ulm University (DE)

Thomas Probst

Winfried Schlee

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

Till Ittermann

Uli Niemann
University of Magdeburg (DE)

Henry VOlzke

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

Danilo Schmidt

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
National and Kapodistrian University of Athens

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
Brunel Unevirsity London (UK)

Yongmin Li

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

Walter D. Leon-Salas

Reza Derakhshani
University of Missouri-Kansas City (USA)

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
National Technical University of Athens (GR)

Athanasios Anastasiou

Ioannis Kouris

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
Aalto University (FI)

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
University of Westminster (UK)

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

Renato Hermoza
Pontifical Catholic University of Peru (PE)

Cesar Beltran Castanon
Pontificia Universidad Católica del Perú (PE)

Luis Cano

Miluska Castillo

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

Lucia Saachi

Riccardo Bellazzi

Luca Chiovato

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

Sonja Zillner
Siemens AG (DE)

Matthias Hammon

Paul Gass

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
University of Cantabria (SP)

Marta Zorrilla

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

Gustavo Marísio Bacelar-Silva

Duarte Nuno Gonçalves-Ferreira

Pedro Vieira-Marques

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
Oncoavanze (SP)

Matilde M. Fernandez

Francisco Monteiro-Guerra
Salumedia Tecnologias (SP)

Luis Fernandez-Luque
Qatar Computing Research Institute (QA)

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
Leipzig University (DE)

Christoph Augenstein

Bogdan Franczyk

Martin Wagner

Martin Apitz

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

Emmanouil Marakakis

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

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

Sven Kalle

Rudiger Pryss
Ulm University (DE)

Winfried Schlee

Thomas Probst

Berthold Langguth

Manfred Reichert

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
University College Dublin (IE)

Ailish Daly

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

Rudiger Pryss
Ulm University (DE)

Johannes Schobel

Winfried Schlee

Thomas Probst

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

Alexandre Augusto Gimenes Marquez Filho

Rodrigo Rodrigues Pires de Mello

Gabriela Bussolo Colonetti

Aldo von Wangenheim

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

Lucas C. Scabora

Mirela T. Cazzolato
University of Sao Paulo - ICMC (BR)

Willian D. Oliveira

Agma J. M. Traina
University of Sao Paulo (BR)

Caetano Traina-Jr
University of Sao Paulo (BR)

Exhibition

teosavv — Tue, 16 May 2017 10:30:48 +0000

Thursday, June 22, 2017 - 14:00 to 20:30

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