Rapp, Michael ; Kulessa, Moritz ; Loza Mencía, Eneldo ; Fürnkranz, Johannes (2022)
Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance.
In: Frontiers in Big Data, 2022, 4
doi: 10.26083/tuprints-00020807
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Rapp, Michael ; Kulessa, Moritz ; Loza Mencía, Eneldo ; Fürnkranz, Johannes |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Frontiers Media |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Big Data |
| Jahrgang/Volume einer Zeitschrift: | 4 |
| Kollation: | 13 Seiten |
| DOI: | 10.26083/tuprints-00020807 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20807 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-208075 |
| Zusätzliche Informationen: | Keywords: outbreak detection, syndromic surveillance, rule learning, knowledge discovery, time series analysis |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 05 Apr 2022 13:09 |
| Letzte Änderung: | 06 Apr 2022 09:28 |
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| Suche nach Titel in: | TUfind oder in Google |
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