Learning healthcare delivery network with longitudinal electronic health records data

Knowledge networks, such as the healthcare delivery network (HDN), describing relationships among different medical encounters, are useful summaries of state-of-art medical knowledge. The increasing availability of longitudinal electronic health records (EHR) data promises a rich data source for learning HDN. Most existing methods for inferring knowledge networks are based on cooccurrence patterns that do not account for temporal effects or patient-level heterogeneity. In this article, building upon the multivariate Hawkes process (mvHP), we propose a flexible covariate-adjusted random effects (CARE) mvHP modeling strategy for HDN construction. Our model allows for patient-specific time-varying background intensity functions via random effects, which can also adjust for effects of important covariates. We adopt a penalized approach to select fixed effects, yielding a sparse network structure, and to remove unnecessary random effects from the model. Through extensive simulation studies, we show that our proposed method performs well in recovering the network structure and that it is essential to account for patient heterogeneities. We further illustrate our CARE mvHP method in an EHR study of type 2 diabetes patients to learn an HDN for these patients and demonstrate that our results are consistent with current clinical practice in healthcare systems.