  Survival analysis focuses on estimating time-to-event distributions which can
help in dynamic risk prediction in healthcare. Extending beyond the classical
Cox model, deep learning techniques have been developed which moved away from
the constraining assumptions of proportional hazards. Traditional statistical
models often only include static information where, in this work, we propose a
novel conditional variational autoencoder-based method called DySurv, which
uses a combination of static and time-series measurements from patient
electronic health records to estimate the risk of death dynamically. DySurv has
been tested on several time-to-event benchmarks where it outperforms existing
methods, including deep learning methods, and we evaluate it on real-world
intensive care unit data from MIMIC-IV and eICU. The predictive capacity of
DySurv is consistent and the survival estimates remain disentangled across
different datasets supporting the idea that dynamic deep learning models based
on conditional variational inference in multi-task cases can be robust models
for survival analysis.
