Learning from imbalanced data in surveillance of nosocomial infection

Summary 

Objective

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.

Methods and material

Standard surveillance strategies are time-consuming and cannot be applied hospital-wide; alternative methods are required. In NI detection viewed as a classification task, the main difficulty resides in the significant imbalance between positive or infected (11%) and negative (89%) cases. To remedy class imbalance, we explore two distinct avenues: (1) a new resampling approach in which both oversampling of rare positives and undersampling of the noninfected majority rely on synthetic cases (prototypes) generated via class-specific subclustering, and (2) a support vector algorithm in which asymmetrical margins are tuned to improve recognition of rare positive cases.

Results and conclusion

Experiments have shown both approaches to be effective for the NI detection problem. Our novel resampling strategies perform remarkably better than classical random resampling. However, they are outperformed by asymmetrical soft margin support vector machines which attained a sensitivity rate of 92%, significantly better than the highest sensitivity (87%) obtained via prototype-based resampling.

Keywords: Nosocomial infection, Machine learning, Support vector machines, Data imbalance