Paper Info
Title
Enhancing the Self-Aware Early Warning Score System Through Fuzzified Data Reliability Assessment.
Abstract
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.
Year
DOI
Venue
2017
10.1007/978-3-319-98551-0_1
Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering
Keywords
Field
DocType
Early Warning Score,Modified early warning score,Self-awareness,Data reliability,Consistency,Plausibility,Fuzzy logic,Hierarchical agent-based system
Monitoring system,Computer science,Data reliability,Fuzzy logic,Self,Early warning score,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
247
1867-8211
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Maximilian Gotzinger132.85
Arman Anzanpour2385.93
Iman Azimi3505.99
Nima Taherinejad44213.58
Amir Masoud Rahmani5787110.98