学习具有多种治疗方法的最佳小组结构个性化治疗规则

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Journal of Machine Learning Research Pub Date : 2023-01-01
Haixu Ma, Donglin Zeng, Yufeng Liu
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引用次数: 0

摘要

近年来,数据驱动的个体化决策问题受到了广泛关注。特别是,决策者的目标是确定最佳个体化治疗规则(ITR),从而最大限度地提高平均于异质性患者特异性特征的预期特定结果。许多现有方法处理二元或中等数量的治疗臂,可能不会考虑潜在的治疗效果结构。然而,当治疗臂数量变多时,这些方法的有效性可能会下降。在本文中,我们提出了 GROWL(Group Outcome Weighted Learning)方法,通过一次优化来估计治疗空间中的潜在结构和最优组结构 ITR。特别是,为了估算组结构 ITR,我们利用基于加强角的多类支持向量机(RAMSVM),在基于加权角的多类分类框架下学习基于组的决策规则。费雪一致性、超额风险约束和价值函数收敛率的建立为 GROWL 提供了理论保证。模拟研究和实际数据分析的大量实证结果表明,GROWL 比其他几种现有方法具有更好的性能。
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Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.

Data driven individualized decision making problems have received a lot of attentions in recent years. In particular, decision makers aim to determine the optimal Individualized Treatment Rule (ITR) so that the expected specified outcome averaging over heterogeneous patient-specific characteristics is maximized. Many existing methods deal with binary or a moderate number of treatment arms and may not take potential treatment effect structure into account. However, the effectiveness of these methods may deteriorate when the number of treatment arms becomes large. In this article, we propose GRoup Outcome Weighted Learning (GROWL) to estimate the latent structure in the treatment space and the optimal group-structured ITRs through a single optimization. In particular, for estimating group-structured ITRs, we utilize the Reinforced Angle based Multicategory Support Vector Machines (RAMSVM) to learn group-based decision rules under the weighted angle based multi-class classification framework. Fisher consistency, the excess risk bound, and the convergence rate of the value function are established to provide a theoretical guarantee for GROWL. Extensive empirical results in simulation studies and real data analysis demonstrate that GROWL enjoys better performance than several other existing methods.

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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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