Point Processes for Competing Observations with Recurrent Networks (POPCORN): A Generative Model of EHR Data

Modeling EHR data is of significant interest in a broad range of applications including prediction of future conditions or building latent representations of patient history. This can be challenging because EHR data is multivariate and irregularly sampled. Traditional treatments of EHR data involve handling irregular sampling by imputation or discretization. In this work, we model the full longitudinal history of a patient using a generative multivariate point process that simultaneously: (1) Models irregularly sampled events probabilistically without discretization or interpolation (2) Has a closed-form likelihood, making training straightforward (3) Encodes dependence between times and events with an approach inspired by competing risk models (4) Allows for direct sampling. We show improved performance on next-event prediction compared to existing approaches. Our pro- posed framework could potentially be used in many different contexts including prediction, generation of synthetic data and building latent representations of patient history.