论文标题
具有电子病历数据的最佳治疗决策的自适应半监督推断
Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data
论文作者
论文摘要
治疗方案是根据患者根据协变量信息为治疗分配治疗的规则。最近，对最佳治疗方案产生最大的总体预期临床结果的估计引起了很多关注。在这项工作中，我们考虑在半监督环境下使用电子病历数据对最佳治疗方案进行估计。在这里，数据由两个部分组成：我们拥有协变量，治疗和结果信息的一组“标记”患者，以及一组更大的“未标记”患者，我们只有协变量信息。我们提出了一种基于插定的半监督方法，利用“未标记”个体来获得对最佳治疗方案的更有效估计器。提供了所提出的估计量及其相关推理程序的渐近特性。进行了仿真研究，以评估所提出方法的经验性能，并仅使用标记的数据与完全监督的方法进行比较。还为进一步说明提供了对重症监护病房（ICU）期间降压发作（ICU）期间降压发作的应用的应用。
A treatment regime is a rule that assigns a treatment to patients based on their covariate information. Recently, estimation of the optimal treatment regime that yields the greatest overall expected clinical outcome of interest has attracted a lot of attention. In this work, we consider estimation of the optimal treatment regime with electronic medical record data under a semi-supervised setting. Here, data consist of two parts: a set of `labeled' patients for whom we have the covariate, treatment and outcome information, and a much larger set of `unlabeled' patients for whom we only have the covariate information. We proposes an imputation-based semi-supervised method, utilizing `unlabeled' individuals to obtain a more efficient estimator of the optimal treatment regime. The asymptotic properties of the proposed estimators and their associated inference procedure are provided. Simulation studies are conducted to assess the empirical performance of the proposed method and to compare with a fully supervised method using only the labeled data. An application to an electronic medical record data set on the treatment of hypotensive episodes during intensive care unit (ICU) stays is also given for further illustration.
