Collaborative Research: Semiparametric and Reinforcement Learning for Precision Medicine

精准医疗旨在针对个体特征（包括遗传特征、人口统计信息、环境因素等）进行优化医疗。个体化治疗规则形式化了将患者信息转化为推荐治疗的决策过程，动态治疗方案由一个或多个治疗决策时间的个体化治疗决策序列组成。与此同时，医学成像技术的最新发展极大地影响了疾病和健康研究。生物医学成像和成像引导干预是精准医学基础设施的关键。在精密医学研究中，开发一种将影像数据与其他丰富信息相结合的方法是非常重要的。然而，目前在精准医学研究中对上述丰富特征的探索还远远不够。基于此，本项目旨在构建具有丰富特征的精准医学统计分析框架，为数据驱动决策提供支持，不仅丰富统计方法学研究，而且为健康科学领域的治疗和生活方式干预提供综合的早期诊断工具和信息工具。此外，该项目还将为研究生提供培训和支持，并指导本科生和研究生水平的课程。pi将把q学习、半参数学习、功能数据分析和强化学习框架应用于具有丰富特征的精准医学，包括医学图像、遗传特征、人口信息、环境因素等。针对不同的场景，本研究项目包括三个组成部分：(i)包含丰富特征的功能个性化治疗方案研究，并开发一种新的基础扩展工具来处理多维图像特征；（ii）包含丰富特征的广义功能个体化治疗方案研究，使响应变量离散；（iii）具有丰富特征的功能q学习，将方法扩展到多阶段决策设置。研究人员将进行理论发展，开发有效的算法，并实施和应用该项目中所有这些组件的实际数据工具。从统计学的角度来看，理论探索将使我们对具有丰富特征的精准医学中的半参数学习和强化学习有更多的认识。从计算的角度来看，高效和可扩展的算法将以公开可用的软件的形式开发和实现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Precision medicine seeks to optimize the medical treatments tailored to individual characteristics, including genetic features, demographic information, environmental factors, etc. Individualized treatment rule formalizes the process of decision making that translates the patients’ information into the recommended treatment, and a dynamic treatment regime consists of the sequence of individualized treatment decisions for one or more treatment decision times. Meanwhile, recent developments in medical imaging technologies dramatically affect disease and health studies. Biomedical imaging and imaging-guided interventions are key in the infrastructure for precision medicine. It is of great importance to developing an approach for incorporating imaging data along with other abundant information in precision medicine research. However, the current exploration for these aforementioned abundant features in precision medicine study is far from sufficient. Motivated by this, the project targets to build the statistical analysis framework in precision medicine incorporating abundant features and provide the support of data-driven decision making, which will not enrich statistical methodological studies but provide an integrated early diagnosis tool and an informative tool to guide treatment and lifestyle intervention in health science. In addition, the project will provide training and support for graduate students, as well as instructions in both undergraduate- and graduate-level courses.The PIs will adapt the Q-learning, semiparametric learning, functional data analysis, and reinforcement learning frameworks to precision medicine with abundant features, including medical images, genetic features, demographic information, environmental factors, etc. Focusing on different scenarios, this research program consists of three components: (i) functional individualized treatment regime study incorporating abundant features, along with the development of a novel basis expansion tool to handle the multi-dimensional image feature; (ii) generalized functional individualized treatment regime study incorporating abundant features, which allows the response variable discrete; and (iii) functional Q-learning with abundant features, which extends the methodology to the multi-stage decision setting. The investigators will conduct the theoretical developments, develop efficient algorithms, and implement and apply the tools to real-world data for all these components in this project. From the statistical point of view, the theoretical explorations will yield more insights into semiparametric and reinforcement learning in precision medicine with abundant features. From the computational point of view, efficient and scalable algorithms will be developed and implemented in a form of publicly available software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.