Collaborative Research: Semiparametric and Reinforcement Learning for Precision Medicine

协作研究：精准医学的半参数和强化学习

• 批准号: 2210658
• 负责人: Xinyi Li
• 金额: $ 13.39万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210658&HistoricalAwards=false
• 关键词: Collaborative; Research; Semiparametric; Reinforcement; Learning

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.

精准医疗旨在根据个体特征（包括遗传特征、人口统计信息、环境因素等）优化医疗治疗方案。个体化治疗规则将决策过程形式化，将患者的信息转化为推荐的治疗方案和动态的治疗方案包括一个或多个治疗决策时间的一系列个体化治疗决策。同时，医学成像技术的最新发展极大地促进了疾病和健康研究，是精准医学基础设施的关键。开发整合成像方法的重要性然而，目前对精准医学研究中这些丰富特征的探索还远远不够，因此该项目的目标是构建包含丰富特征和特征的精准医学统计分析框架。提供数据驱动决策的支持，这不会丰富统计方法学研究，而是提供综合的早期诊断工具和指导健康科学治疗和生活方式干预的信息工具。此外，该项目将为研究生提供培训和支持。学生，以及本科生和研究生水平的指导PI将把Q-learning、半参数学习、功能数据分析、强化学习框架应用到具有丰富特征的精准医疗中，包括医学图像、遗传特征、人口统计信息、环境因素等。针对不同场景，研究计划由三个部分组成：（i）结合丰富特征的功能个体化治疗方案研究，以及开发处理多维图像特征的新型基础扩展工具；（ii）结合丰富特征的广义功能个体化治疗方案研究； features ，允许响应变量离散； (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，并将工具应用于所有这些组件的实际数据。从统计的角度来看，理论探索将为精准医学中的半参数和强化学习提供更多的见解，从计算的角度来看，将以一种形式开发和实现高效且可扩展的算法。公开可用的软件。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。