Dynamic Survival Transformers for Causal Inference with Electronic Health Records

• URL: http://arxiv.org/abs/2210.15417v1
• Date: Tue, 25 Oct 2022 18:17:46 GMT
• Title: Dynamic Survival Transformers for Causal Inference with Electronic Health Records
• Authors: Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier
• Abstract summary: We introduce the Dynamic Survival Transformer (DynST), a deep survival model that trains on electronic health records. We derive a semi-synthetic EHR dataset from MIMIC-III to show that DynST can accurately estimate the causal effect of a treatment intervention.
• Score: 19.89643522713678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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