Constructing Disease Network and Temporal Progression Model via Context-Sensitive Hawkes Process

Abstract

Modeling disease relationships and temporal progression are two key problems in health analytics, which have not been studied together due to data and technical challenges. Thanks to the increasing adoption of Electronic Health Records (EHR), rich patient information is being collected over time. Using EHR data as input, we propose a multivariate context-sensitive Hawkes process or cHawkes, which simultaneously infers the disease relationship network and models temporal progression of patients. Besides learning disease network and temporal progression model, cHawkes is able to predict when a specific patient might have other related diseases in future given the patient history, which in turn can have many potential applications in predictive health analytics, public health policy development and customized patient care. Extensive experiments on real EHR data demonstrate that cHawkes not only can uncover meaningful disease relations and model accurate temporal progression of patients, but also has significantly better predictive performance compared to several baseline models.