Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms.
JSON
Hashemi AS, Ghazani MM, Ohlsson M, Björk J, Dietler D
Stud Health Technol Inform 316 (-) 1916-1920
[2024-08-22; online 2024-08-23]
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.
PubMed 39176866
DOI 10.3233/SHTI240807
Crossref 10.3233/SHTI240807
pii: SHTI240807