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: 2210659
• 负责人: Michael Kosorok
• 金额: $ 26万
• 依托单位: University of North Carolina at Chapel Hill
• 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=2210659&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.

精密医学旨在优化针对个人特征的医疗治疗，包括遗传特征，人口统计信息，环境因素等。个性化治疗规则正式化了将患者的信息转化为建议治疗的决策过程，并且动态治疗方案由一个或更多的治疗治疗决策时间组成一个或更多的治疗决策时间。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的干预措施是精密医学基础设施的关键。在精确医学研究中开发一种将成像数据以及其他丰富信息结合在一起的方法非常重要。但是，当前对这些合理的丰富特征在精密医学研究中的探索还远远不够。在此激励的基础上，该项目的目标是在精确医学中建立统计分析框架，其中包含丰富的特征，并提供了数据驱动的决策制定，这将不会丰富统计方法论研究，而是提供了一种综合的早期诊断工具，并提供了一个信息，以指导治疗和生活方式干预健康科学。 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)功能性个性化处理制度研究编码丰富的特征，以及开发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性化处理制度研究，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，并实施并将工具应用于本项目中所有这些组件的实际数据。从统计的角度来看，理论探索将在具有丰富特征的精确医学中对半参数和强化学习产生更多的见解。从计算的角度来看，将以公开可用软件的形式开发和实施高效且可扩展的算法。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估审查标准，认为通过评估被认为是宝贵的支持。