Reducing COPD Readmissions: A Causal Bayesian Network Model

Abstract

This letter introduces a causal Bayesian network model to study readmissions reduction for chronic obstructive pulmonary disease (COPD) patients. The model employs a Bayesian network learning method and adopts domain knowledge. Using this model, we analyze the impacts of critical variables on a patient's readmission risk by the manipulation of such variables. Through this analysis, effective intervention options to reduce readmission can be identified, which can provide a quantitative tool for designing personalized interventions to reduce COPD readmissions.