ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

Abstract

Unplanned readmissions to the emergency department (ED) have been identified as a key factor resulting in a negative effect on subjects’ health and healthcare resource scheduling. Often, the readmission prediction is modeled as a binary classification problem whose objective is to predict if a subject will be readmitted or not. Nevertheless, it ignores the uncertainty nature of readmission and usually results in poor prediction quality. In this paper, the problem is defined as a chance-constrained medical intervention rationing problem: at-risk subjects are targeted and given supplemental medical interventions, while the remaining subjects are treated as outpatients. The objective is to profile subjects, identify at-risk subjects, and select specific groups of subjects to which additional medical interventions are recommended, while addressing the unknown number of at-risk subjects and the unknown subjects’ readmission risks. We propose a white-box approach named Alternating Clustering and Bayesian Inference (ACBI) and investigate its efficiency on a real-life readmission data set. Results are promising and show the method could lead up to a 34.42% reduction in readmission rate.