Forest-Based Point Process for Event Prediction from Electronic Health Records

Accurate prediction of future onset of disease from Electronic Health Records (EHRs) has important clinical and economic implications. In this domain the arrival of data comes at semi-irregular intervals and makes the prediction task challenging. We propose a method called multiplicative-forest point processes (MFPPs) that learns the rate of future events based on an event history. MFPPs join previous theory in multiplicative forest continuous-time Bayesian networks and piecewisecontinuous conditional intensity models. We analyze the advantages of using MFPPs over previous methods and show that on synthetic and real EHR forecasting of heart attacks, MFPPs outperform earlier methods and augment off-the-shelf machine learning algorithms.