论文标题
Auton-Survival：用于回归，反事实估计，评估和表型的开源软件包，并通过审查的活动时间数据
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
论文作者
论文摘要
机器学习在医疗保健中的应用通常需要处理时间到事实的预测任务，包括不良事件的预测，重新住院或死亡。由于失去随访，这种结果通常受到审查。标准的机器学习方法不能直接地应用于具有审查结果的数据集。在本文中，我们介绍了Auton-Survival，这是一个开源存储库，用于简化与经过审查的活动时间或生存数据一起工作的工具。 Auton Survival包括用于生存回归的工具，存在域移位，反事实估计，风险分层的表型，评估以及治疗效果的估计。通过现实世界中的案例研究，采用了大量的SEER肿瘤学发病率数据，我们证明了Auton Survival迅速支持数据科学家在回答复杂健康和流行病学问题方面的能力。
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.
