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