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Applications of machine learning in healthcare often require working withtime-to-event prediction tasks including prognostication of an adverse event,re-hospitalization or death. Such outcomes are typically subject to censoringdue to loss of follow up. Standard machine learning methods cannot be appliedin a straightforward manner to datasets with censored outcomes. In this paper,we present auton-survival, an open-source repository of tools to streamlineworking with censored time-to-event or survival data. auton-survival includestools for survival regression, adjustment in the presence of domain shift,counterfactual estimation, phenotyping for risk stratification, evaluation, aswell as estimation of treatment effects. Through real world case studiesemploying a large subset of the SEER oncology incidence data, we demonstratethe ability of auton-survival to rapidly support data scientists in answeringcomplex health and epidemiological questions.

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data