Confidence-Enhanced Early Warning Score Based on Fuzzy Logic




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

PublisherSpringer US

2022

Mobile Networks and Applications

Mobile Networks and Applications

27

691

708

1383-469X

1572-8153

DOIhttps://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.


Last updated on 2024-26-11 at 17:06