Confidence-Enhanced Early Warning Score Based on Fuzzy Logic
: Götzinger Maximilian, Anzanpour Arman, Azimi Iman, TaheriNejad Nima, Jantsch Axel, Rahmani Amir M., Liljeberg Pasi
Publisher: Springer US
: 2022
: Mobile Networks and Applications
: Mobile Networks and Applications
: 27
: 691
: 708
: 1383-469X
: 1572-8153
DOI: https://doi.org/10.1007/s11036-019-01324-5(external)
: https://doi.org/10.1007/s11036-019-01324-5(external)
: https://research.utu.fi/converis/portal/detail/Publication/42633276(external)
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.