ST2 - Social data and medical data analytics

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