Mining minority enriched AllofUs data for innovative ethnic specific risk prediction modeling

挖掘少数族裔丰富的 AllofUs 数据，用于创新的种族特定风险预测模型

• 批准号: 10798514
• 负责人: Jue Hou
• 金额: $ 23.95万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10798514
• 关键词: Accounting; Address; Algorithms; All of Us Research Program; Cardiotoxicity; Caring; Clinical; Clinical Data; Code; Communication; Computer software; Data; Data Analyses; Development; Diagnosis; Electronic Health Record; Engineering; Ethnic Origin; Ethnic Population; Genetic; Genetic Heterogeneity; Genetic Risk; Goals; Guide prevention; Health; Healthcare; Healthcare Systems; Heterogeneity; Individual; Intervention; Learning; Link; Malignant Neoplasms; Medical Genetics; Methods; Mining; Minority; Minority Groups; Minority Participation; Modeling; Onset of illness; Outcome; Patient Outcomes Assessments; Patient risk; Patients; Pattern; Performance; Population; Race; Reporting; Research; Research Design; Research Project Grants; Resolution; Rheumatoid Arthritis; Risk; Risk Estimate; Risk Factors; Sample Size; Source; Surveys; Toes; Training; Validation; case-based; cohort; data exchange; data harmonization; design; economic disparity; empowerment; evidence base; feature selection; genetic information; genetic risk factor; genome sequencing; genome wide association study; health equity; improved; innovation; learning strategy; malignant breast neoplasm; multimodal data; multimodality; open source; overtreatment; patient population; point of care; precision medicine; predictive tools; privacy preservation; racial population; risk prediction; risk prediction model; risk stratification; social; social health determinants; statistics; tool; trait; transfer learning; whole genome; Accounting; Address; Algorithms; All of Us Research Program; Cardiotoxicity; Caring; Clinical; Clinical Data; Code; Communication; Computer software; Data; Data Analyses; Development; Diagnosis; Electronic Health Record; Engineering; Ethnic Origin; Ethnic Population; Genetic; Genetic Heterogeneity; Genetic Risk; Goals; Guide prevention; Health; Healthcare; Healthcare Systems; Heterogeneity; Individual; Intervention; Learning; Link; Malignant Neoplasms; Medical Genetics; Methods; Mining; Minority; Minority Groups; Minority Participation; Modeling; Onset of illness; Outcome; Patient Outcomes Assessments; Patient risk; Patients; Pattern; Performance; Population; Race; Reporting; Research; Research Design; Research Project Grants; Resolution; Rheumatoid Arthritis; Risk; Risk Estimate; Risk Factors; Sample Size; Source; Surveys; Toes; Training; Validation; case-based; cohort; data exchange; data harmonization; design; economic disparity; empowerment; evidence base; feature selection; genetic information; genetic risk factor; genome sequencing; genome wide association study; health equity; improved; innovation; learning strategy; malignant breast neoplasm; multimodal data; multimodality; open source; overtreatment; patient population; point of care; precision medicine; predictive tools; privacy preservation; racial population; risk prediction; risk prediction model; risk stratification; social; social health determinants; statistics; tool; trait; transfer learning; whole genome

PROJECT SUMMARY/ABSTRACT Advancement of health equity requires evidence and tools tailored for minority groups. The shift towards individualized precision medicine requires risk prediction tools to guide prevention and intervention. Due to the genetic heterogeneity and social economic disparity, risk factors may disproportionately impact race/ethnicity (R/E) groups. Overall risk prediction constructed from predominantly white populations can perform poorly on other ethnic groups, leading to mis-diagnosis, over-treatment and other adverse health consequences. Efforts on developing R/E-specific risk prediction at local healthcare systems are limited by the small sample size caused by inadequate representability of minority populations. To address the gap and to advance precision medicine for non-white patients, it is crucial to harness minority enriched clinical data and develop risk models transferable to point of care. The All of Us (AoU) program offers a wealth of comprehensive multi-modal data on whole genome sequencing (WGS), real-world electronic health records (EHR) and patient reported outcomes (PRO) with enhanced minority participation, providing the common evidence base for learning general R/E-specific risk patterns and training risk models for minority populations at local healthcare systems. In this proposal, we develop innovative methods for risk modeling in AoU data tailored for minority populations and its validation on external healthcare data. We will showcase the proposed methods in two use cases: 1) rheumatoid arthritis (RA) genome-wide association study (GWAS) at Mass General Brigham (MGB) focusing on the genetic risk factors; 2) cancer cardiotoxicity prediction study at M Health Fairview (MHF) focusing on clinical and social determinants of health (SDoH) risk factors. In Aim 1, we integrate risk factor and disease onset outcome data across WGS, EHR and PRO in AoU data to construct the risk prediction model that yields better risk prediction accuracy, risk factor identification and fairness across R/E groups. In Aim 2, we design privacy preserving algorithms to validate the generalizability risk modeling from AoU data on external healthcare data and establish the transfer learning strategy to adapt AoU risk models for local healthcare systems. We intend for the methods to facilitate development of risk modeling using AoU data with focus on minority populations, as well as toe demonstrate the potential impact of the AoU program on improving care at local healthcare.

项目摘要/摘要 健康公平的进步需要为少数群体量身定制的证据和工具。转向 个性化的精密医学需要指导预防和干预的风险预测工具。由于 遗传异质性和社会经济差异，危险因素可能不成比例地影响种族/种族 （R/E）组。主要是白人种群构建的总体风险预测的性能很差 其他种族，导致错误诊断，过度治疗和其他不良健康后果。努力 在开发R/E特异性风险预测时，在当地医疗保健系统中受到较小样本量的限制 由少数群体的代表性不足引起。解决差距并提高精度 非白人患者的药物，这对于利用少数族裔富含临床数据和开发风险模型至关重要 可以转移到护理点。我们所有人（AOU）计划提供了大量全面的多模式数据 全基因组测序（WGS），现实世界电子健康记录（EHR）和患者报道 成果（Pro）具有增强的少数群体参与，为学习提供了共同的证据基础 当地医疗保健系统中少数族裔人口的一般R/E特异性风险模式和培训风险模型。 在此提案中，我们开发了针对少数族裔人口量身定制的AOU数据进行风险建模的创新方法 及其对外部医疗保健数据的验证。我们将在两种用例中展示提出的方法：1） 大规模杨百翰（MGB）聚焦的类风湿关节炎（RA）全基因组关联研究（GWAS） 关于遗传危险因素； 2）M Health Fairview（MHF）的癌症心脏毒性预测研究 健康的临床和社会决定因素（SDOH）风险因素。在AIM 1中，我们整合了风险因素和疾病 跨WGS，EHR和PRO的发作结果数据在AOU数据中构建了产生的风险预测模型 更好的风险预测准确性，风险因素识别和跨R/E组的公平性。在AIM 2中，我们设计 隐私保存算法以验证外部数据的AOU数据验证概括性风险建模 医疗保健数据并建立转移学习策略以适应当地医疗保健风险模型 系统。我们打算采用使用AOU数据来促进风险建模的方法，重点是 少数群体以及脚趾表明了AOU计划对改善护理的潜在影响 当地医疗保健。