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一项具有里程碑意义的联邦跨部门合作,旨在促进医疗保健领域的数据科学发展:百万退伍军人计划——用于医疗精准性以改善当前治疗效果的计算健康分析。

A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

作者信息

Justice Amy C, McMahon Benjamin, Madduri Ravi, Crivelli Silvia, Damrauer Scott, Cho Kelly, Ramoni Rachel, Muralidhar Sumitra

机构信息

VA Connecticut Healthcare System, West Haven, CT 06516, United States.

Yale School of Medicine and Public Health, Yale University, New Haven, CT 06510, United States.

出版信息

JAMIA Open. 2024 Nov 6;7(4):ooae126. doi: 10.1093/jamiaopen/ooae126. eCollection 2024 Dec.

Abstract

OBJECTIVES

In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration.

MATERIALS AND METHODS

Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals. The program supported centralized data management and genomic analysis including creation of a scalable approach to cataloging phenotypes. Cross-cutting methods including natural language processing, image processing, and reusable code were developed.

RESULTS

The 79.6 million dollar collaboration has supported centralized data management and genomic analysis including a scalable approach to cataloging phenotypes and launched over 10 collaborative scientific projects in health conditions highly prevalent in veterans. A ground-breaking analysis on the Summit and Andes supercomputers at the Oak Ridge National Laboratory (ORNL) of the genetic underpinnings of over 2000 health conditions across 44 million genetic variants which resulted in the identification of 38 270 independent genetic variants associating with one or more health traits. Of these, over 2000 identified associations were unique to non-European ancestry. Cross-cutting methods have advanced state-of-the-art artificial intelligence (AI) including large language natural language processing and a system biology study focused on opioid addiction awarded the 2018 Gordon Bell Prize for outstanding achievement in high-performance computing. The collaboration has completed work in prostate cancer, suicide prevention, and cardiovascular disease, and cross-cutting data science. Predictive models developed in these projects are being tested for application in clinical management.

DISCUSSION

Eight new projects were launched in 2023, taking advantage of the momentum generated by the previous collaboration. A major challenge has been limitations in the scope of appropriated funds at DOE which cannot currently be used for health research.

CONCLUSION

Extensive multidisciplinary interactions take time to establish and are essential to continued progress. New funding models for maintaining high-performance computing infrastructure at the ORNL and for supporting continued collaboration by joint VA-DOE research teams are needed.

摘要

目标

2016年,美国退伍军人事务部(VA)和能源部(DOE)达成了一项跨部门协议(IAA),即百万退伍军人计划 - 医疗精准计算健康分析以改善当前结果(MVP-CHAMPION)研究合作。

材料与方法

监督工作由VA研究发展办公室(VA ORD)和DOE总部负责。一个执行委员会和2名高级科学联络官与VA和DOE的领导层合作,以优化各项工作,服务于共同的科学目标。该计划支持集中式数据管理和基因组分析,包括创建一种可扩展的方法来编目表型。还开发了包括自然语言处理、图像处理和可重复使用代码在内的交叉方法。

结果

这项7960万美元的合作支持了集中式数据管理和基因组分析,包括一种可扩展的编目表型方法,并在退伍军人中高度流行的健康状况领域启动了10多个合作科学项目。在橡树岭国家实验室(ORNL)的Summit和Andes超级计算机上,对4400万个基因变异中的2000多种健康状况的遗传基础进行了开创性分析,结果鉴定出38270个与一种或多种健康特征相关的独立基因变异。其中,超过2000个已鉴定的关联在非欧洲血统人群中是独特的。交叉方法推动了包括大语言自然语言处理在内的前沿人工智能(AI)发展,一项专注于阿片类药物成瘾的系统生物学研究获得了2018年戈登贝尔高性能计算杰出成就奖。该合作已完成前列腺癌、自杀预防和心血管疾病以及交叉数据科学方面的工作。这些项目中开发的预测模型正在进行临床管理应用测试。

讨论

2023年启动了8个新项目,借助了先前合作产生的势头。一个主要挑战是DOE拨款资金范围的限制,目前这些资金不能用于健康研究。

结论

广泛的多学科互动需要时间来建立,并且对于持续进展至关重要。需要新的资金模式来维持ORNL的高性能计算基础设施,并支持VA-DOE联合研究团队的持续合作。

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