In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assumptions about the data-generating process and cannot capture complex interactions among patient covariates. We introduce the Dynamic Survival Transformer (DynST), a deep survival model that trains on electronic health records (EHRs). Unlike previous transformers used in survival analysis, DynST can make use of time-varying information to predict evolving survival probabilities. We derive a semi-synthetic EHR dataset from MIMIC-III to show that DynST can accurately estimate the causal effect of a treatment intervention on restricted mean survival time (RMST). We demonstrate that DynST achieves better predictive and causal estimation than two alternative models.
@article{arxiv.2210.15417,
title = {Dynamic Survival Transformers for Causal Inference with Electronic Health Records},
author = {Prayag Chatha and Yixin Wang and Zhenke Wu and Jeffrey Regier},
journal= {arXiv preprint arXiv:2210.15417},
year = {2022}
}
Comments
Accepted to the NeurIPS 2022 Workshop on Learning from Time Series for Health