auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
This provides a practical toolkit for data scientists working on healthcare prediction tasks with censored outcomes, though it is incremental as it packages existing methods into a unified framework.
The authors tackled the problem of applying machine learning to censored time-to-event data in healthcare by developing auton-survival, an open-source package that includes tools for survival regression, counterfactual estimation, and evaluation, demonstrating its utility with real-world SEER oncology data.
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.