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

项目总结/摘要 促进健康公平需要针对少数群体的证据和工具。转向 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/族裔 （R/E）基团。从主要是白色人群构建的总体风险预测在以下人群中表现不佳： 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发R/E特定风险预测的研究受到样本量小的限制 这是由于少数民族的代表性不足造成的。解决差距并提高精度 对于非白人患者，利用少数族裔丰富的临床数据并开发风险模型至关重要 可转移到护理点。All of Us（AoU）计划提供了丰富的综合多模态数据 全基因组测序（WGS），真实世界的电子健康记录（EHR）和患者报告 提高少数群体的参与，为学习提供共同的证据基础 一般R/E特定的风险模式和培训风险模型，为少数群体在当地医疗保健系统。 在这项提案中，我们开发了针对少数群体量身定制的AoU数据风险建模的创新方法 以及其在外部医疗保健数据上的验证。我们将在两个用例中展示所提出的方法：1） 马萨诸塞州布里格姆（MGB）的类风湿性关节炎（RA）全基因组关联研究（GWAS）， 遗传风险因素; 2）M Health Fairview（MHF）的癌症心脏毒性预测研究，重点是 健康的临床和社会决定因素（SDoH）风险因素。在目标1中，我们整合了风险因素和疾病 AoU数据中WGS、EHR和PRO的发病结局数据，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R/E组之间的公平性。在目标2中，我们设计 隐私保护算法，以验证来自外部AoU数据的概化风险建模 医疗保健数据，并建立迁移学习策略，以适应当地医疗保健的AoU风险模型 系统.我们打算使用AoU数据来促进风险建模的开发，重点是 少数民族人口，以及证明AoU计划对改善护理的潜在影响， 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.