Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms.

Autor:	Hashemi AS; Center for Applied Intelligent Systems Research, Halmstad University, Sweden.; Division of Occupational and Environmental Medicine, Lund University, Sweden., Ghazani MM; Center for Applied Intelligent Systems Research, Halmstad University, Sweden., Ohlsson M; Center for Applied Intelligent Systems Research, Halmstad University, Sweden.; Centre for Environmental and Climate Science, Lund University, Sweden., Björk J; Division of Occupational and Environmental Medicine, Lund University, Sweden.; Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden., Dietler D; Division of Occupational and Environmental Medicine, Lund University, Sweden.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 1916-1920.
DOI:	10.3233/SHTI240807
Abstrakt:	Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection. Subsequently, an advanced anomaly detection technique is applied incrementally to these time-series symptoms as univariate and multivariate approaches to assess the effectiveness of a machine learning-based method on early detection of the COVID-19 outbreak.
Databáze:	MEDLINE
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