SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction. (arXiv:2204.09633v2 [cs.LG] UPDATED)

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 …

arxiv cancer data event lg prediction risks time