  Effective learning from electronic health records (EHR) data for prediction
of clinical outcomes is often challenging because of features recorded at
irregular timesteps and loss to follow-up as well as competing events such as
death or disease progression. To that end, we propose a generative
time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential
Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively
parameterize dynamics of latent states under irregularly sampled input data.
Our model then utilizes the resulting latent embedding to flexibly estimate
survival times for multiple competing events without specifying shapes of
event-specific hazard function. We demonstrate competitive performance of our
model on MIMIC-III, a freely-available longitudinal dataset collected from
critical care units, on predicting hospital mortality as well as the data from
the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous
Thromboembolism (VTE), a life-threatening complication for patients with
cancer, with death as a competing event. SurvLatent ODE outperforms the current
clinical standard Khorana Risk scores for stratifying VTE risk groups, while
providing clinically meaningful and interpretable latent representations.
