Explainable learning for disease risk prediction based on comorbidity networks

Abstract

Disease risk modeling is of great interest to clinicians and healthcare policy makers in reducing preventable harm and associated costs. A disease risk prediction model is preferable if it (1) exhibits superior prediction performance and (2) constructs explainable rules to allow medical professionals to understand why and how the prediction was made. Existing studies usually focus on one of the two features. In this study, we propose a comorbidity network involved end-to-end trained disease risk prediction model. The adoption of side information and the end-to-end framework together ensure both high accuracy and transparency to the model. The prediction performances of the proposed model are demonstrated by using a real case study based on three years of medical histories from the Hong Kong Hospital Authority is considered. Results show that the proposed model exhibits superior prediction performance while learns explainable rules.