Enhancing the Self-Aware Early Warning Score System Through Fuzzified Data Reliability Assessment
| Autor: | Iman Azimi, Amir M. Rahmani, Arman Anzanpour, Maximilian Götzinger, Nima TaheriNejad |
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| Rok vydání: | 2018 |
| Předmět: |
Measure (data warehouse)
Computer science business.industry Data reliability 020206 networking & telecommunications Monitoring system 02 engineering and technology Machine learning computer.software_genre Early warning score Fuzzy logic 020202 computer hardware & architecture Consistency (database systems) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Reliability (statistics) |
| Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319985503 MobiHealth |
| DOI: | 10.1007/978-3-319-98551-0_1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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