使用观察数据对个性化治疗规则进行有效的强化和放松学习。

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2019-01-01
Ying-Qi Zhao, Eric B Laber, Yang Ning, Sumona Saha, Bruce E Sands
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引用次数: 0

摘要

个性化治疗规则旨在确定是否、何时、哪种治疗以及治疗对象。全球人口老龄化、医疗成本上升以及对患者层面数据的访问增加,迫切需要能够应用于观察数据的个性化治疗规则的高质量估计。最近一项有希望的估计个性化治疗规则的研究将估计最佳治疗规则的问题重新定义为加权分类问题。我们考虑了一类最优处理规则的估计量,它类似于凸的大边缘分类器。所提出的类别适用于观察数据,并且在倾向或结果模型的正确规范导致最佳个体化治疗规则的一致估计的意义上是双重稳健的。使用半参数效率理论中的技术,我们导出了所提出的估计量的收敛率,并使用这些收敛率来表征使用基于分类的方法估计个性化治疗规则的偏差-方差权衡。根据这些结果进行的模拟实验表明,在所提出的框架内构建新的估计量是可能的,其显著优于现有的估计量。我们使用来自分娩训练计划和炎症性肠综合征研究的数据来说明所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access to patient-level data have created an urgent need for high-quality estimators of individualized treatment rules that can be applied to observational data. A recent and promising line of research for estimating individualized treatment rules recasts the problem of estimating an optimal treatment rule as a weighted classification problem. We consider a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers. The proposed class applies to observational data and is doubly-robust in the sense that correct specification of either a propensity or outcome model leads to consistent estimation of the optimal individualized treatment rule. Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods. Simulation experiments informed by these results demonstrate that it is possible to construct new estimators within the proposed framework that significantly outperform existing ones. We illustrate the proposed methods using data from a labor training program and a study of inflammatory bowel syndrome.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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