A probabilistic ontology-based platform for self-learning context-aware healthcare applications

•The architecture of a self-learning, ontology-based framework is presented.•Framework enables context-aware applications to change their behavior at run-time.•Knowledge added to the ontology is given a probability to convey its reliability.•An implemented use case is presented: mining the reasons f...

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Bibliographic Details
Published in:	Expert systems with applications Vol. 40; no. 18; pp. 7629 - 7646
Main Authors:	Ongenae, Femke, Claeys, Maxim, Dupont, Thomas, Kerckhove, Wannes, Verhoeve, Piet, Dhaene, Tom, De Turck, Filip
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier Ltd 15.12.2013 Elsevier
Subjects:	Algorithms Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy Applied sciences Biological and medical sciences Computer science; control theory; systems Computerized, statistical medical data processing and models in biomedicine Context-aware Data processing. List processing. Character string processing Dynamics eHealth Emergency and intensive care: infection, septic shock Exact sciences and technology Expert systems Health care Information systems. Data bases Intensive care medicine Medical computing and teaching Medical sciences Memory organisation. Data processing Nurse call system Nurses Ontology Platforms Probabilistic methods Probability Probability theory Self-learning Software Trends Workflow Probability Nurse call system Self-learning Ontology Context-aware eHealth Data mining Modeling Business process Sepsis syndrome Behavioral analysis World wide web Probability learning Dynamic model Public health User need Execution time Self learning Probabilistic approach Behavior change Context aware Integrated management Infection User requirement Reliability User behavior
ISSN:	0957-4174, 1873-6793
Online Access:	Get full text
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