Refereed journal article or data article (A1)

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic




List of Authors: Maximilian Götzinger, Arman Anzanpour, Iman Azimi, Nima TaheriNejad, Axel Jantsch, Amir M. Rahmani, Pasi Liljeberg

Publisher: Springer US

Publication year: 2019

Journal: Mobile Networks and Applications

Journal name in source: Mobile Networks and Applications

ISSN: 1383-469X

eISSN: 1572-8153

DOI: http://dx.doi.org/10.1007/s11036-019-01324-5

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/42633276


Abstract

Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.


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Last updated on 2022-16-08 at 10:26