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.
翻译:在保健中应用机器学习往往需要与时间到活动预测任务一起工作,包括预测不利事件、重新住院或死亡等。这类结果通常由于后续行动的丧失而接受审查。标准机器学习方法不能直接适用于带有审查结果的数据集。在本文中,我们介绍了自动生存,这是一个开放源码的储存库,可以简化受审查的时间到活动或生存数据的工作。自动生存包括生存回归工具、在存在域变换的情况下进行调整、反事实估计、风险分级、评估以及治疗效果估计。通过实际世界案例研究,我们利用大量SEER肿瘤事件数据,展示了自动生存能力,迅速支持数据科学家应对复杂的健康和流行病问题。