Federated learning methods for heterogeneous and distributed Medicaid data

异构分布式医疗补助数据的联邦学习方法

• 批准号: 10590354
• 负责人: Lu Tang
• 金额: $ 18.93万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590354
• 关键词: Address; Big Data; Buprenorphine; Cells; Characteristics; Clinical; Collection; Complex; Computer software; Confidence Intervals; Data; Data Sources; Decision Making; Dedications; Dose; Ensure; Environment; Formulation; Goals; Health; Health Policy; Heterogeneity; Hospitals; Individual; Learning; Left; Low income; Machine Learning; Medicaid; Meta-Analysis; Methodology; Methods; Modeling; Opioid; Outcome; Overdose; Overdose reduction; Participant; Pennsylvania; Pharmaceutical Preparations; Policies; Population; Population Heterogeneity; Population Research; Precision Health; Recommendation; Research; Research Personnel; Safety; Sampling; Scheme; Site; Source; Subgroup; System; Testing; Translational Research; US State; Uncertainty; Variant; Vulnerable Populations; buprenorphine treatment; data integration; demographics; distributed data; diverse data; effectiveness clinical trial; empowerment; federated learning; heterogenous data; improved; improved outcome; individualized medicine; information model; innovation; insight; learning strategy; medication for opioid use disorder; mortality; multiple data sources; novel strategies; opioid overdose; opioid use disorder; overdose death; overdose prevention; personalized approach; precision medicine; programs; randomized, clinical trials; repository; social health determinants; success; treatment choice; treatment effect; user friendly software; user-friendly

Project Summary The broad objective of this project is to develop federated learning approaches that can efficiently reduce uncertainty and improve generalizability when assessing treatment effects based on multiple data sources. The proposal is motivated by a study of the Medicaid Outcome Distributed Research Network (MODRN) of eleven states in assessing the quality and access of medications for opioid use disorder (OUD). The collection of Medicaid claims data accounts for 40% of the OUD population in the US and covers a wide array of treatment choices, making it an ideal data source for understanding subgroup-specific treatment effects and developing precision health strategies. We leverage this large-scale distributed research network (DRN) to investigate the heterogeneous treatment effect (HTE) of buprenorphine, an opioid-based medication, on overdose mortality. However, the extra source of heterogeneity across states due to variation in state policy environments, which is largely unobserved, has presented great challenge in the assessment of HTE. Existing approaches such as meta-analysis are inadequate and underpowered to address the translational research needs in understanding the complex interactions among treatments, clinical characteristics and social determinant of health, especially, under the heavy influence of unexplainable heterogeneity across states. A suite of novel approaches will be developed to address a wide range of analytical requests that support data-driven precision health research under the framework of federated learning, where states collaboratively build analytical models under the orchestration of a coordinating state without pooling individual-participant data. With a central goal of modeling for different levels of heterogeneity in DRNs, this project focuses on the following aims: 1. To develop and evaluate a high-precision HTE estimator for buprenorphine for Pennsylvania by incorporating modeling information from ten other states; 2. To develop and evaluate a generalizable treatment recommendation system that protects vulnerable populations and is robust to policy variation across states. The methods will be rigorously tested and delivered as user friendly statistical software. The proposed methods extend well beyond MODRN and easily find applications in other common DRNs, such as hospital data networks and mobile data networks.

项目摘要 该项目的主要目标是开发联邦学习方法， 在基于多个数据源评估治疗效果时，的 该提案的动机是对11个医疗补助结果分布式研究网络（MODRN）的研究。 评估阿片类药物使用障碍（OUD）药物的质量和可及性。收集 Medicaid索赔数据占美国OUD人口的40%，涵盖广泛的治疗 选择，使其成为了解亚组特定治疗效果和开发 精准健康战略。我们利用这个大规模的分布式研究网络（DRN）来调查 丁丙诺啡（一种阿片类药物）对过量死亡率的异质性治疗效应（HTE）。 然而，由于州政策环境的变化， 在很大程度上未被观察到，这对HTE的评估提出了很大的挑战。现有的方法，如 元分析是不充分的和不足的，以解决翻译研究的需要，在理解 治疗、临床特征和健康的社会决定因素之间的复杂相互作用，特别是， 在各州之间无法解释的异质性的严重影响下。一套新颖的方法将是 旨在满足广泛的分析需求，支持数据驱动的精准健康研究 在联邦学习的框架下，各国在联邦学习框架下合作建立分析模型， 协调状态的编排，而无需汇集单个参与者数据。其中心目标是 针对DRN中不同层次的异质性，本项目的主要目标是：1.制定和 通过合并建模评估宾夕法尼亚州丁丙诺啡的高精度HTE估计值 来自其他10个国家的信息; 2.制定和评估可推广的治疗建议 这是一个保护弱势群体的制度，对各国的政策变化具有鲁棒性。方法将是 经过严格测试，并作为用户友好的统计软件提供。所提出的方法远远超出了 MODRN，并很容易在其他常见的DRN中找到应用，例如医院数据网络和移动的数据 网络.