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 50.6% on this problem, the one-class approach increased sensitivity to as much as 92.6%. These results are comparable to those obtained by the authors in a previous study on asymmetrical soft margin SVMs; they suggest that one-class SVMs can provide an effective and efficient way of overcoming data imbalance in classification problems.