Anomaly detection in clinical processes.

Meaningful anomalies in clinical processes may be related to caring performance or even the patient survival. It is imperative that the anomalies be timely detected such that useful and actionable knowledge of interest could be extracted to clinicians. Many previous approaches assume prior knowledge about the structure of clinical processes, using which anomalies are detected in a supervised manner. For a majority of clinical settings, however, clinical processes are complex, ad hoc, and even unknown a prior. In this paper, we investigate how to facilitate detection of anomalies in an unsupervised manner. An anomaly detection model is presented by applying a density-based clustering method on patient careflow logs. Using the learned model, it is possible to detect whether a particular patient careflow trace is anomalous with respect to normal traces in the logs. The approach has been validated over real data sets collected from a Chinese hospital.