Data Imbalance in Surveillance of Nosocomial Infections

An important problem that arises in hospitals is the monitoring and detection of nosocomial or hospital acquired infections (NIs). 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 classification task, the main difficulty resides in the significant imbalance between positive or infected (11%) and negative (89%) cases. To remedy class imbalance, we propose a novel approach in which both oversampling of rare positives and undersampling of the non infected majority rely on synthetic cases generated via class-specific subclustering. Experiments have shown this approach to be remarkably more effective than classical random resampling methods.