Abstract
Early Warning Score (EWS) system is specified to detect and predict patient deterioration in hospitals. This is achievable via monitoring patient’s vital signs continuously and is often manually done with paper and pen. However, because of the constraints in healthcare resources and the high hospital costs, the patient might not be hospitalized for the whole period of the treatments, which has lead to a demand for in-home or portable EWS systems. Such a personalized EWS system needs to monitor the patient at anytime and anywhere even when the patient is carrying out daily activities. In this paper, we propose a self-aware EWS system which is the reinforced version of the existing EWS systems by using the Internet of Things technologies and the self-awareness concept. Our self-aware approach provides (i) system adaptivity with respect to various situations and (ii) system personalization by paying attention to critical parameters. We evaluate the proposed EWS system using a full system demonstration.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Azimi, I., Anzanpour, A., Rahmani, A.M., Liljeberg, P., Tenhunen, H. (2017). Self-aware Early Warning Score System for IoT-Based Personalized Healthcare. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_8
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DOI: https://doi.org/10.1007/978-3-319-49655-9_8
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