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An application of one-class support vector machine to nosocomial infection detection

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An application of one-class support vector machine to nosocomial infection detection

COHEN, Gilles, et al.

Abstract

Nosocomial infections (NIs)---those acquired in health care settings---are among the major causes of increased mortality among hospitalized patients. They are a significant burden for patients and health authorities alike; it is thus important to monitor and detect them through an effective surveillance system. This paper describes a retrospective analysis of a prevalence survey of NIs done in the Geneva University Hospital. Our goal is to identify patients with one or more NIs on the basis of clinical and other data collected during the survey. In this two-class classification task, the main difficulty lies in the significant imbalance between positive or infected (11%) and negative (89%) cases. To cope with class imbalance, we investigate one-class SVMs which can be trained to distinguish two classes on the basis of examples from a single class (in this case, only "normal" or non infected patients). The infected ones are then identified as "abnormal" cases or outliers that deviate significantly from the normal profile. Experimental results are encouraging: whereas standard 2-class SVMs scored a baseline sensitivity of [...]

COHEN, Gilles, et al . An application of one-class support vector machine to nosocomial

infection detection. Studies in Health Technology and Informatics , 2004, vol. 107, no. Pt 1, p. 716-720

PMID : 15360906

DOI : 10.3233/978-1-60750-949-3-716

Available at:

http://archive-ouverte.unige.ch/unige:9303

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