Vienna, Austria; June 24, 2017
|Frank van Harmelen |
Knowledge Representation & Reasoning
Computer Science Department
Vrije Universiteit Amsterdam
How Linked (Open?) Data can benefit Healthcare systems
A steady progress in semantic technologies over the past decade and a half has resulted in stable syntactic and semantic models for publishing and interlinking datasets on the web. Such interlinked and interoperable datasets have had a significant impact on a large number of technology sectors: e-commerce, cultural heritage, science, media and publishing, just to name a few.
However, the impact of linked data on healthcare information systems has been limited so far in comparison with these other sectors. In this talk I will argue that Linked Data technologies can also be very useful for a variety of applications in healthcare information systems, and that this is even true (perhaps surprisingly) for Linked *Open* Data.
||Welcome by Organizers|
||Session I: Keynote|
|How Linked (Open?) Data can benefit Healthcare Systems
Frank van Harmelen
||Session II: Temporal Analysis and Representation. Simulation.|
|(L) Temporal Conformance Analysis and Explanation on Comorbid Patients.
Luca Piovesan, Paolo Terenziani, and Daniele Theseider Dupré.
(S) Representing and reasoning with probabilistic temporal distances for guideline interaction analysis.
Paolo Terenziani, Antonella Andolina.
(S) Utilizing Temporal Genomic Data for Healthcare.
Guenter Tusch and Shahrzad Eslamian.
(S) Representation of Temporal Constraints in Clinical Guidelines using openEHR archetypes and GDL.
Lilian Mie Mukai Cintho, Claudia Moro, Lucia Sacchi, and Silvana Quaglini.
(S) A Knowledge-Based Conceptual Model to Simulate Circulatory Shock.
David Riaño, José Ramón Alonso.
||Session III: Modeling and Decision Support. Data Analysis.|
A Data- and Expert-driven Decision Support Framework for Helping
Patients Adhere to Therapy: Psychobehavioral Targets and Associated
Szymon Wilk, Dympna O’Sullivan, Martin Michalowski, Silvia Bonaccio, Wojtek Michalowski, Mor Peleg, and Marc Carrier.
(L) A general framework for the distributed management of exceptions and comorbidities.
A. Bottrighi, L. Piovesan, P. Terenziani.
(S) A Label Taxonomy to Support Collaborative Formalization of Computer-interpretable Guidelines and Classification of Modeling Issues.
Arturo González-Ferrer, María Ángel Valcárcel, Henar González-Luengo, and Enea Parimbelli.
(S) Performance Analysis of Markov Chains-based Models in Process Mining: A Case Study.
Roberto Gatta, Jacopo Lenkowicz, Mauro Vallati, Eric Rojas, Andrea Damiani, Carlos Fernandez-Llatas, Lucia Sacchi, Berardino De Bari, Arianna Dagliati, and Vincenzo Valentini.
(S) A Self-Enforcing Network for the Analysis of Fall Cases in a University Hospital.
Christina Klüver and Christian Dahlmann.
||Session IV: Natural Language Processing and Deep Learning|
|(L) Towards automatic clinical operation encoding (CHOP) with convolutional neural networks.
Yihan Deng, André Sander, Lukas Faulstich, and Kerstin Denecke.
(L) Corpus Annotation for Aspect Based Sentiment Analysis in Medical Domain.
Salud María Jiménez-Zafra, M. Teresa Martín-Valdivia, M. Dolores, Molina-González, and L. Alfonso Ureña López.
(S) Analyzing cancer forum discussions with text mining.
Gerard van Oortmerssen, Stephan Raaijmakers, Maya Sappelli, Erik Boertjes, Suzan Verberne, Nicole Walasek, and Wessel Kraaij.
(S) Design of an Information Extraction Pipeline for German Clinical Texts.
Madeleine Kittner, Bariya Bajwa, Damian Rieke, Mario Lamping, Johannes Starlinger, and Ulf Leser.
||Closure by Organizers|
Long presentations (L): 20 min. for exposition + 10 min. for questions.
Short presentations (S): 10 min. for exposition + 5 min. for questions.