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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.

项目总结