A rule-based approach for self-optimisation in autonomic eHealth systems
English
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) >]
2018
CD-ROM Edition
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
151-154
Yes
No
International
978-3-8007-4559-3
SAOS 2018 - 6th International Workshop on "Self-Optimisation in Autonomic & Organic Computing Systems" in ARCS 2018 - 31st International Conference on Architecture of Computing Systems
[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.