CDSM -- Casual Inference using Deep Bayesian Dynamic Survival Models

A smart healthcare system that supports clinicians for risk-calibrated treatment assessment typically requires the accurate modeling of time-to-event outcomes. To tackle this sequential treatment effect estimation problem, we developed causal dynamic survival model (CDSM) for causal inference with survival outcomes using longitudinal electronic health record (EHR). CDSM has impressive explanatory performance while maintaining the prediction capability of conventional binary neural network predictors. It borrows the strength from explanatory framework including the survival analysis and counterfactual framework and integrates them with the prediction power from a deep Bayesian recurrent neural network to extract implicit knowledge from EHR data. In two large clinical cohort studies, our model identified the conditional average treatment effect in accordance with previous literature yet detected individual effect heterogeneity over time and patient subgroups. The model provides individualized and clinically interpretable treatment effect estimations to improve patient outcomes.