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• W2971302419 abstract "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." @default.
• W2971302419 created "2019-09-05" @default.
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• W2971302419 date "2019-08-29" @default.
• W2971302419 modified "2023-09-25" @default.
• W2971302419 title "Confidence-Enhanced Early Warning Score Based on Fuzzy Logic" @default.
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• W2971302419 doi "https://doi.org/10.1007/s11036-019-01324-5" @default.
• W2971302419 hasPublicationYear "2019" @default.
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