A4 Refereed article in a conference publication
Enhancing the Self-Aware Early Warning Score System through Fuzzified Data Reliability Assessment
Authors: Maximilian Götzinger, Arman Anzanpour, Iman Azimi, Nima TaheriNejad, Amir M. Rahman
Editors: Paolo Perego, Amir Rahmani, Nima TaheriNejad
Conference name: International Conference on Wireless Mobile Communication and Healthcare
Publication year: 2018
Journal: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Book title : Wireless Mobile Communication and Healthcare : 7th International Conference, MobiHealth 2017, Vienna, Austria, November 14–15, 2017, Proceedings
Series title: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume: 247
First page : 3
Last page: 11
ISBN: 978-3-319-98550-3
eISBN: 978-3-319-98551-0
ISSN: 1867-8211
DOI: https://doi.org/10.1007/978-3-319-98551-0_1
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/29164061
Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.
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