Predictive Monitoring of Local Anomalies in Clinical Treatment Processes

Local anomalies are small outliers that exist in some subsegments of clinical treatment processes (CTPs). They provide crucial information to medical staff and hospital managers for determining the efficient medical service delivered to individual patients, and for promptly handling unusual treatment behaviors in CTPs. Existing studies mainly focused on the detection of large deviations of CTPs, called of global anomalous inpatient traces. However, local anomalies in inpatient traces are easily overlooked by existing approaches. In some medical problems, such as unstable angina, local anomalies are important since they may indicate unexpected changes of patients’ physical conditions. In this work, we propose a predictive monitoring service on local anomalies using a Latent Dirichlet Allocation (LDA)-based probabilistic model. The proposal was evaluated in the study of unstable angina CTP, testing 12,152 patient traces from the Chinese PLA General Hospital.