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Effective learning from electronic health records (EHR) data for predictionof clinical outcomes is often challenging because of features recorded atirregular timesteps and loss to follow-up as well as competing events such asdeath or disease progression. To that end, we propose a generativetime-to-event model, SurvLatent ODE, which adopts an Ordinary DifferentialEquation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectivelyparameterize dynamics of latent states under irregularly sampled input data.Our model then utilizes the resulting latent embedding to flexibly estimatesurvival times for multiple competing events without specifying shapes ofevent-specific hazard function. We demonstrate competitive performance of ourmodel on MIMIC-III, a freely-available longitudinal dataset collected fromcritical care units, on predicting hospital mortality as well as the data fromthe Dana-Farber Cancer Institute (DFCI) on predicting onset of VenousThromboembolism (VTE), a life-threatening complication for patients withcancer, with death as a competing event. SurvLatent ODE outperforms the currentclinical standard Khorana Risk scores for stratifying VTE risk groups, whileproviding clinically meaningful and interpretable latent representations.

SurvLatent ODE : A Neural ODE based time-to-event model with competing  risks for longitudinal data improves cancer-associated Venous Thromboembolism  (VTE) prediction