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

Hashemi, Atiye Sadat LU ; Ghazani, Mirfarid Musavian LU ; Ohlsson, Mattias LU orcid ; Björk, Jonas LU and Dietler, Dominik LU orcid (2024) 34th Medical Informatics Europe Conference, MIE 2024 In Studies in Health Technology and Informatics 316. p.1916-1920
Abstract

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... (More)

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.

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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Anomaly detection, Anomaly transformer, COVID-19 pandemic, Incremental learning, Public health surveillance
host publication
Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024
series title
Studies in Health Technology and Informatics
editor
Mantas, John ; Hasman, Arie ; Demiris, George ; Saranto, Kaija ; Marschollek, Michael ; Arvanitis, Theodoros N. ; Ognjanovic, Ivana ; Benis, Arriel ; Gallos, Parisis ; Zoulias, Emmanouil and Andrikopoulou, Elisavet
volume
316
pages
5 pages
publisher
IOS Press
conference name
34th Medical Informatics Europe Conference, MIE 2024
conference location
Athens, Greece
conference dates
2024-08-25 - 2024-08-29
external identifiers
  • pmid:39176866
  • scopus:85202005899
ISSN
0926-9630
1879-8365
ISBN
9781643685335
DOI
10.3233/SHTI240807
project
Improved preparedness for future pandemics and other health crises through large-scale disease surveillance
Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data
eSSENCE@LU 10:6 - Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data
language
English
LU publication?
yes
id
936889b5-41a7-4afe-a229-4e8cb7e2caee
date added to LUP
2024-10-28 13:33:59
date last changed
2025-03-04 00:51:37
@inproceedings{936889b5-41a7-4afe-a229-4e8cb7e2caee,
  abstract     = {{<p>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.</p>}},
  author       = {{Hashemi, Atiye Sadat and Ghazani, Mirfarid Musavian and Ohlsson, Mattias and Björk, Jonas and Dietler, Dominik}},
  booktitle    = {{Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024}},
  editor       = {{Mantas, John and Hasman, Arie and Demiris, George and Saranto, Kaija and Marschollek, Michael and Arvanitis, Theodoros N. and Ognjanovic, Ivana and Benis, Arriel and Gallos, Parisis and Zoulias, Emmanouil and Andrikopoulou, Elisavet}},
  isbn         = {{9781643685335}},
  issn         = {{0926-9630}},
  keywords     = {{Anomaly detection; Anomaly transformer; COVID-19 pandemic; Incremental learning; Public health surveillance}},
  language     = {{eng}},
  pages        = {{1916--1920}},
  publisher    = {{IOS Press}},
  series       = {{Studies in Health Technology and Informatics}},
  title        = {{Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms}},
  url          = {{http://dx.doi.org/10.3233/SHTI240807}},
  doi          = {{10.3233/SHTI240807}},
  volume       = {{316}},
  year         = {{2024}},
}