Reference : A rule-based approach for self-optimisation in autonomic eHealth systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/35484

Title :	A rule-based approach for self-optimisation in autonomic eHealth systems
Language :	English
Author, co-author :	Neyens, Gilles [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Zampunieris, Denis [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2018
Edition :	CD-ROM Edition
Main document title :	Workshop Proceedings ot the 6th International Workshop on "Self-Optimisation in Autonomic & Organic Computing Systems" in ARCS 2018 - 31st International Conference on Architecture of Computing Systems, Braunschweig, Germany, 09 - 12 April, 2018
Pages :	151-154
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-3-8007-4559-3
Event name :	SAOS 2018 - 6th International Workshop on "Self-Optimisation in Autonomic & Organic Computing Systems" in ARCS 2018 - 31st International Conference on Architecture of Computing Systems
Event date :	09 - 12 April, 2018
Event organizer :	Technical University of Braunschweig
Event place (city) :	Braunschweig
Event country :	Germany
Keywords :	[en] Self-optimisation ; Rule-based Systems ; Sensor Mediation ; Proactive Computing
Abstract :	[en] Advances in machine learning techniques in recent years were of great benefit for the detection of diseases/medical conditions in eHealth systems, but only to a limited extend. In fact, while for the detection of some diseases the data mining techniques were performing very well, they still got outperformed by medical experts in about half of the tests done. In this paper, we propose a hybrid approach, which will use a rule-based system on top of the machine learning techniques in order to optimise the results of conflict handling. The goal is to insert the knowledge from medical experts in order to optimise the results given by the classification techniques. Possible positive and negative effects will be discussed.
Permalink :	http://hdl.handle.net/10993/35484

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