A More Perfect Union: Leveraging Clinically Deployed Models and Cancer Epidemiology Cohort Data to Improve AI/ML Readiness of NIH-Supported Population Sciences Resources

项目总结/摘要 NIH已经在大规模前瞻性观察队列中投入了数亿美元。这些 研究的多样化和有价值的数据被用来产生关于生活方式和 环境影响健康和疾病。这些高维和多模态的真实世界数据可以使 广泛的研究，包括新的AI/ML应用。不幸的是，标准的方法用于存储， 管理、分析和共享他们的数据对于当代AI/ML的使用并不理想。这就形成了一种“准备就绪 这一差距阻碍了新的AI/ML研究。该项目提出了一种创新但可行的方法， 通过在多个层面上提高AI/ML准备程度来弥补这一差距。我们的多学科团队包括City的AI/ML专家 来自加州教师研究（CTS）队列团队的经验丰富的人口科学家; 来自圣地亚哥超级计算机中心（SDSC）的云计算专家Sherlock Cloud。的CTS 包括133，477名女性参与者，自1995年以来一直持续跟踪。通过调查和 联系，CTS收集了全面的暴露和生活方式数据，并确定了28，000多个 癌症;超过34，000例死亡;超过800，000例住院治疗。基于AI/ML就绪性 框架，我们将更新CTS的数据和计算架构;重新配置数据探索， 聚合工具和文档;并使用CTS数据来文本化、评估和扩展现有的临床 AI/ML模型。首先，我们将扩展当前的私有CTS数据分析云， 可扩展的计算环境，专门用于AI/ML。我们将部署Amazon Web Services（AWS）资源 在我们安全的CTS飞地内实现AI/ML，并提供运行全套科学技术的支持GPU的实例 Python和Python笔记本中的计算和AI/ML包。其次，我们将在 CTS数据，以降低数据复杂性，这是AI/ML应用程序的障碍。嵌入性很低- 多维潜在表示，将来自多个模态的数据压缩成表示 参与者数据的紧凑嵌入或抽象摘要。使用无监督学习和 自动编码器深度神经网络将CTS数据聚类到基于表型的子组中， 基本的AI/ML功能，如队列发现、近邻识别和插补。三是 在COH增加临床部署的风险模型（例如，对于再入院）与CTS数据直接评估 真实世界队列数据的潜力，以提高模型性能和临床模型的可移植性， 队列人群。这三项举措中的每一项都将记录在交互式教程笔记本中， 对研究界公平。该项目包括人员、流程和 技术：一个由相关领域专家组成的新的多学科小组;新的通用嵌入 观察性队列数据的表示;以及专门配置的安全的基于云的基础设施 新的AI/ML项目这项工作的成功完成将缩小队列数据的AI/ML准备差距。

PROJECT SUMMARY/ABSTRACT NIH has invested hundreds of millions of dollars in large-scale prospective observational cohorts. These studies' diverse and valuable data have been used to generate important discoveries about how lifestyle and environment affect health and disease. These high-dimensional and multi-modal real-world data can enable broad research, including new AI/ML applications. Unfortunately, the standard methods cohorts use to store, manage, analyze, and share their data are not ideal for contemporary AI/ML use. This creates a “readiness gap” that hinders new AI/ML research. This project proposes an innovative yet feasible approach to close that gap by improving AI/ML readiness at multiple levels. Our multidisciplinary team includes AI/ML experts at City of Hope (COH); experienced population scientists from the California Teachers Study (CTS) cohort team; and cloud computing specialists from the San Diego Supercomputer Center's (SDSC) Sherlock Cloud. The CTS includes 133,477 female participants who have been followed continuously since 1995. Through surveys and linkages, the CTS has collected comprehensive exposure and lifestyle data and has identified over 28,000 cancers; over 34,000 deaths; and over 800,000 individual hospitalizations. Based on an AI/ML readiness framework, we will update the CTS's data & computing architecture; reconfigure data exploration and aggregation tools and documentation; and use CTS data to text, evaluate, and expand existing, clinically deployed AI/ML models. First, we will expand the current private CTS data analytics cloud to include a new scalable computing environment specifically for AI/ML. We will deploy Amazon Web Services (AWS) resources for AI/ML within our secure CTS enclave and provision GPU-enabled instances running a full suite of scientific computing and AI/ML packages in Python and Jupyter Notebooks. Second, we will generate embeddings in the CTS data to reduce the data complexity that is a barrier to AI/ML applications. Embeddings are low- dimensional latent representations that compress data from multiple modalities into vectors that represent a compact embedding, or abstracted summary, of a participant's data. Use of unsupervised learning and an autocoder deep neural network will cluster CTS data into phenotype-based subgroups that can be used for essential AI/ML functions, such as cohort discovery, close-neighbor identification, and imputation. Third, we will augment clinically deployed risk models at COH (e.g., for readmissions) with CTS data to directly evaluate the potential for real-world cohort data to improve model performance and the portability of clinical models into cohort populations. Each of these three initiatives will be documented in interactive tutorial notebooks that will be FAIR for the research community. This project includes a balanced combination of people, process, and technology: a new multidisciplinary team of experts from relevant fields; new general-purpose embedding representations of observational cohort data; and a secure cloud-based infrastructure configured specifically for new AI/ML projects. Successful completion of this work will close the AI/ML readiness gap for cohort data.