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Enhancing the Self-Aware Early Warning Score System Through Fuzzified Data Reliability Assessment

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 247)

Abstract

Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.

Keywords

  • Early Warning Score
  • Modified early warning score
  • Self-awareness
  • Data reliability
  • Consistency
  • Plausibility
  • Fuzzy logic
  • Hierarchical agent-based system

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Notes

  1. 1.

    The reliability module in our implementation limits the cross-reliability \(r_{cro}\) to a value between 0 to 1, although theoretically, a coefficient less than 1 can lead to a \(r_{cro}\) higher that 1.

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Acknowledgement

The authors wish to acknowledge the financial support by the Marie Curie Actions of the European Union’s H2020 Programme.

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Correspondence to Maximilian Götzinger .

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Götzinger, M., Anzanpour, A., Azimi, I., TaheriNejad, N., Rahmani, A.M. (2018). Enhancing the Self-Aware Early Warning Score System Through Fuzzified Data Reliability Assessment. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-98551-0_1

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