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Dynamic Survival Transformers for Causal Inference with Electronic Health Records. (arXiv:2210.15417v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv causal inference electronic health inference records survival transformers