Abstract
Early Warning Score (EWS) systems are utilized in hospitals by health-care professionals to interpret vital signals of patients. These scores are used to measure and predict amelioration or deterioration of patients’ health status to intervene in an appropriate manner when needed. Based on an earlier work presenting an automated Internet-of-Things based EWS system, we propose an architecture to analyze and enhance data reliability and consistency. In particular, we present a hierarchical agent-based data confidence evaluation system to detect erroneous or irrelevant vital signal measurements. In our extensive experiments, we demonstrate how our system offers a more robust EWS monitoring system.
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Notes
- 1.
We note that the consequence of an anomaly detection should be/is decided by higher levels of the system. Regardless, the observation unit needs to alert the higher levels.
- 2.
http://research.omicsgroup.org/index.php/List_of_weather_records, accessed on July 2016.
- 3.
We remark that to ascertain a signal’s rate of change, a history is needed. As a preparatory work, history has to get smoothed before calculating the rates of change, otherwise, noise could affect this measurement.
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Götzinger, M., Taherinejad, N., Rahmani, A.M., Liljeberg, P., Jantsch, A., Tenhunen, H. (2017). Enhancing the Early Warning Score System Using Data Confidence. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_12
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