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Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
KTH.
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2018 (English)In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 24, no 1, p. 24-42Article in journal (Refereed) Published
Abstract [en]

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7percent recall, 79.7percent precision and 85.7percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

Place, publisher, year, edition, pages
Sage Publications, 2018. Vol. 24, no 1, p. 24-42
Keywords [en]
clinical decision-making, databases and data mining, ehealth, electronic health records, secondary care
National Category
Infectious Medicine
Identifiers
URN: urn:nbn:se:kth:diva-223483DOI: 10.1177/1460458216656471ISI: 000424053900003PubMedID: 27496862Scopus ID: 2-s2.0-85041631179OAI: oai:DiVA.org:kth-223483DiVA, id: diva2:1185054
Funder
VINNOVA, 2012-01252
Note

QC 20180223

Available from: 2018-02-23 Created: 2018-02-23 Last updated: 2018-02-23Bibliographically approved

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  • Other locale
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Output format
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