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

项目摘要/摘要 促进卫生公平需要为少数群体量身定做的证据和工具。向以下转变 个性化精准医疗需要风险预测工具来指导预防和干预。由于 遗传异质性和社会经济差异，风险因素可能不成比例地影响种族/民族 (R/E)组。从以白人为主的人群构建的总体风险预测在以下方面表现不佳 其他族裔群体，导致误诊、过度治疗和其他不良健康后果。努力 在当地医疗保健系统开展针对R/E的风险预测受制于小样本量 由于少数群体代表性不足造成的。以弥补差距并提高精度 对于非白人患者，关键是利用少数民族丰富的临床数据和开发风险模型 可转移到护理点。我们所有人(AOU)计划提供了丰富的综合多模式数据 关于全基因组测序(WGS)、真实世界电子健康记录(EHR)和患者报告 加强少数群体参与的成果(PRO)，为学习提供共同的证据基础 针对当地卫生保健系统少数群体的一般风险模式和培训风险模型。 在这项建议中，我们为少数族裔人群量身定做的AOU数据中开发了风险建模的创新方法 以及它对外部医疗保健数据的验证。我们将在两个使用案例中展示建议的方法：1) 麻省理工学院布里格姆分校(MGB)的类风湿性关节炎(RA)全基因组关联研究(GWAS) 关于遗传风险因素；2)M Health Fairview(MHF)关注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。在目标1中，我们将风险因素和疾病结合起来 在AOU数据中跨WGS、EHR和PRO的起始结果数据，以构建风险预测模型 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。在目标2中，我们设计了 隐私保护算法用于验证外部AOU数据的概化风险建模 医疗数据和建立迁移学习策略以适应当地医疗保健的AOU风险模型 系统。我们打算使用这些方法来促进使用AOU数据进行风险建模的开发，重点是 少数群体以及评估目标证明了AOU计划在改善保健方面的潜在影响 当地的医疗保健。

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.