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

Atiye Sadat Hashemi; Mirfarid Musavian Ghazani; Mattias Ohlsson; Jonas Björk; Dominik Dietler

doi:10.3233/SHTI240807

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

Atiye Sadat Hashemi, Mirfarid Musavian Ghazani, Mattias Ohlsson, Jonas Björk, Dominik Dietler

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024
Redaktörer	John Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros N. Arvanitis, Ivana Ognjanovic, Arriel Benis, Parisis Gallos, Emmanouil Zoulias, Elisavet Andrikopoulou
Förlag	IOS Press
Sidor	1916-1920
Antal sidor	5
ISBN (elektroniskt)	9781643685335
DOI	https://doi.org/10.3233/SHTI240807
Status	Published - 2024 aug.
Evenemang	34th Medical Informatics Europe Conference, MIE 2024 - Athens, Grekland Varaktighet: 2024 aug. 25 → 2024 aug. 29

Publikationsserier

Namn	Studies in Health Technology and Informatics
Volym	316
ISSN (tryckt)	0926-9630
ISSN (elektroniskt)	1879-8365

Konferens

Konferens	34th Medical Informatics Europe Conference, MIE 2024
Land/Territorium	Grekland
Ort	Athens
Period	2024/08/25 → 2024/08/29

Ämnesklassifikation (UKÄ)

Datavetenskap (Datalogi)

FN:s Globala mål

Denna forskningsoutput relaterar till följande Globala mål

Tillgång till dokument

10.3233/SHTI240807

Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data
Dietler, D. (PI)
Myndigheten för samhällsskydd och beredskap (MSB)
2024/06/01 → 2026/05/31
Projekt: Forskning
eSSENCE@LU 10:6 - Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data
Dietler, D. (PI), Mazhar, S. (Forskare), Björk, J. (Forskare), Litins'ka, Y. (Forskare), Ohlsson, M. (Forskare), Sjödin, H. (Forskare) & Rocklöv, J. (Forskare)
2024/01/01 → 2025/12/31
Projekt: Forskning
PREPARE: PREPARE - Förbättrad beredskap för framtida pandemier och andra hälsokriser genom storskalig sjukdomsövervakning
Björk, J. (Forskare), Inghammar, M. (Forskare), Litins'ka, Y. (Forskare), Forsell, M. (Forskare), Ohlsson, M. (Forskare), Lingman, M. (Forskare), Wigren Byström, J. (Forskare), Almgren, M. (Forskare), Kahn, F. (Forskare), Rasmussen, M. (Forskare), Malmqvist, U. (Forskare), Mitchell, A. (Forskare), Neumann, A. (Forskare), Dietler, D. (Forskare), Bennet, L. (Forskare), Bonander, C. (Forskare), Eriksson, T. (Forskare), Björkelund, A. (Forskare), Vilhelmsson, A. (Forskare), Nilsson, A. (Forskare), Wetterberg, H. (Forskare), Hassan, M. (Forskarstuderande), Hashemi, A. S. (Forskare), Mazhar, S. (Forskare) & Johansson, A. (Forskare)
Swedish Research Council
2021/01/01 → 2025/12/31
Projekt: Forskning

Citera det här

Hashemi, A. S., Ghazani, M. M., Ohlsson, M., Björk, J., & Dietler, D. (2024). Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. I J. Mantas, A. Hasman, G. Demiris, K. Saranto, M. Marschollek, T. N. Arvanitis, I. Ognjanovic, A. Benis, P. Gallos, E. Zoulias, & E. Andrikopoulou (Red.), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024 (s. 1916-1920). (Studies in Health Technology and Informatics; Vol. 316). IOS Press. https://doi.org/10.3233/SHTI240807

Hashemi, Atiye Sadat ; Ghazani, Mirfarid Musavian ; Ohlsson, Mattias et al. / Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. redaktör / John Mantas ; Arie Hasman ; George Demiris ; Kaija Saranto ; Michael Marschollek ; Theodoros N. Arvanitis ; Ivana Ognjanovic ; Arriel Benis ; Parisis Gallos ; Emmanouil Zoulias ; Elisavet Andrikopoulou. IOS Press, 2024. s. 1916-1920 (Studies in Health Technology and Informatics).

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title = "Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms",

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 COVID-19 outbreak.",

keywords = "Anomaly detection, Anomaly transformer, COVID-19 pandemic, Incremental learning, Public health surveillance",

author = "Hashemi, {Atiye Sadat} and Ghazani, {Mirfarid Musavian} and Mattias Ohlsson and Jonas Bj{\"o}rk and Dominik Dietler",

year = "2024",

month = aug,

doi = "10.3233/SHTI240807",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "1916--1920",

editor = "John Mantas and Arie Hasman and George Demiris and Kaija Saranto and Michael Marschollek and Arvanitis, {Theodoros N.} and Ivana Ognjanovic and Arriel Benis and Parisis Gallos and Emmanouil Zoulias and Elisavet Andrikopoulou",

booktitle = "Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024",

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note = "34th Medical Informatics Europe Conference, MIE 2024 ; Conference date: 25-08-2024 Through 29-08-2024",

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Hashemi, AS , Ghazani, MM , Ohlsson, M , Björk, J & Dietler, D 2024, Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. i J Mantas, A Hasman, G Demiris, K Saranto, M Marschollek, TN Arvanitis, I Ognjanovic, A Benis, P Gallos, E Zoulias & E Andrikopoulou (red), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. Studies in Health Technology and Informatics, vol. 316, IOS Press, s. 1916-1920, 34th Medical Informatics Europe Conference, MIE 2024, Athens, Grekland, 2024/08/25. https://doi.org/10.3233/SHTI240807

Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. / Hashemi, Atiye Sadat ; Ghazani, Mirfarid Musavian ; Ohlsson, Mattias et al.
Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. red. / John Mantas; Arie Hasman; George Demiris; Kaija Saranto; Michael Marschollek; Theodoros N. Arvanitis; Ivana Ognjanovic; Arriel Benis; Parisis Gallos; Emmanouil Zoulias; Elisavet Andrikopoulou. IOS Press, 2024. s. 1916-1920 (Studies in Health Technology and Informatics; Vol. 316).

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

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AU - Hashemi, Atiye Sadat

AU - Ghazani, Mirfarid Musavian

AU - Ohlsson, Mattias

AU - Björk, Jonas

AU - Dietler, Dominik

PY - 2024/8

Y1 - 2024/8

N2 - 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.

AB - 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.

KW - Anomaly detection

KW - Anomaly transformer

KW - COVID-19 pandemic

KW - Incremental learning

KW - Public health surveillance

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A2 - Mantas, John

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A2 - Demiris, George

A2 - Saranto, Kaija

A2 - Marschollek, Michael

A2 - Arvanitis, Theodoros N.

A2 - Ognjanovic, Ivana

A2 - Benis, Arriel

A2 - Gallos, Parisis

A2 - Zoulias, Emmanouil

A2 - Andrikopoulou, Elisavet

PB - IOS Press

T2 - 34th Medical Informatics Europe Conference, MIE 2024

Y2 - 25 August 2024 through 29 August 2024

ER -

Hashemi AS , Ghazani MM , Ohlsson M , Björk J , Dietler D. Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. I Mantas J, Hasman A, Demiris G, Saranto K, Marschollek M, Arvanitis TN, Ognjanovic I, Benis A, Gallos P, Zoulias E, Andrikopoulou E, redaktörer, Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. IOS Press. 2024. s. 1916-1920. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI240807

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

Sammanfattning

Publikationsserier

Konferens

Ämnesklassifikation (UKÄ)

FN:s Globala mål

Tillgång till dokument

Fingeravtryck

Projekt

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

PREPARE: PREPARE - Förbättrad beredskap för framtida pandemier och andra hälsokriser genom storskalig sjukdomsövervakning

Citera det här