Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

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.

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

“…The accuracy and precision are given by systematic and random error of measurement, respectively. Data can be accurate and precise but still not truthful [28], for instance, if a sensor is working outside of its operating conditions (e.g., a temperature sensor detached from the test object). Data reliability is a piece of meta-data about the trustworthiness of the input data stream.…”

“…History is "recording and studying a series of past events connected to an entity" and enables extracting knowledge from the recorded time series [94]. Identifying trends in the past, understanding time-dependent aspects of the current state, and predicting future conditions [28] may all be supported by utilizing history.…”

“…We developed several self-aware variants of the EWS application, which are able to deal with different kinds of faults [26]- [28]. Those variants and their results are discussed in detail in the referred papers.…”

“…While the previous version calculated the EWS without any modifications, the final version adjusts the EWS in case of a low reliability [28].…”

“…This paper provides an overview of the framework, how it works and how it can be used. The applicability and flexibility of RoSA are demonstrated by two case studies from the fields of human health monitoring [26]- [28] and industrial machine monitoring [29]- [31]. The main contributions of the paper are:…”