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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要该项目的总体目标是开发联邦学习方法，可以有效地减少基于多个数据源评估治疗效果时的不确定性并提高普遍性。这该提案的动机是对医疗补助结果分布式研究网络 (MODRN) 的 11 名成员进行的一项研究各州评估阿片类药物使用障碍 (OUD) 药物的质量和可及性。的集合医疗补助索赔数据占美国 OUD 人口的 40%，涵盖多种治疗选择，使其成为了解亚组特定治疗效果和开发的理想数据源精准健康策略。我们利用这个大规模分布式研究网络（DRN）来调查丁丙诺啡（一种阿片类药物）对过量死亡率的异质治疗效果（HTE）。然而，由于国家政策环境的差异，各州之间存在额外的异质性，很大程度上未被观察到，这对 HTE 的评估提出了巨大的挑战。现有的方法例如荟萃分析不足以满足理解方面的转化研究需求治疗、临床特征和健康社会决定因素之间复杂的相互作用，特别是受到各国之间无法解释的异质性的严重影响。一系列新颖的方法将旨在满足支持数据驱动的精准健康研究的广泛分析要求在联邦学习的框架下，各国协作构建分析模型在不汇集个体参与者数据的情况下编排协调状态。以建模为中心目标针对 DRN 中不同程度的异质性，该项目重点关注以下目标： 1. 开发和通过结合建模来评估宾夕法尼亚州丁丙诺啡的高精度 HTE 估算器来自其他十个州的信息； 2. 制定和评估通用的治疗建议保护弱势群体并对各州政策变化具有鲁棒性的系统。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 并轻松找到其他常见 DRN 中的应用，例如医院数据网络和移动数据网络。