An automated AI/ML platform for multi-researcher collaborations for a NIH BACPAC funded Spine Phenome Project

用于 NIH BACPAC 资助的脊柱表型项目的多研究人员协作的自动化 AI/ML 平台

• 批准号: 10594295
• 负责人: Safdar N. Khan
• 金额: $ 29.27万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-26 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594295
• 关键词: Address; Adherence; Affect; Algorithms; Caring; Chronic low back pain; Clinical; Clinical Data; Clinical Treatment; Cloud Service; Collaborations; Collection; Complex; Data; Data Collection; Data Science; Data Set; Development; Diagnostic; Disease; Documentation; Ecosystem; Ensure; Environment; FAIR principles; Frequencies; Funding; Goals; Grant; Infrastructure; Ingestion; Intervention; Interventional Imaging; Intuition; Low Back Pain; Machine Learning; Manuals; Measures; Medical Care Costs; Modeling; Modernization; Monitor; Motion; Outcome; Pain management; Pathway interactions; Patient-Focused Outcomes; Patients; Persons; Phase; Phenotype; Prevalence; Process; Provider; Readiness; Research; Research Personnel; Site; Strategic Planning; System; Time; Time Series Analysis; Treatment outcome; United States National Institutes of Health; Vertebral column; application programming interface; biopsychosocial; biopsychosocial factor; clinical decision-making; clinical practice; cost; data access; data cleaning; data quality; data resource; data sharing; digital health; disability; feasibility testing; file format; imaging modality; improved; interest; large datasets; member; novel; pain patient; parent grant; personalized medicine; phenome; prototype; research clinical testing; technology development; usability

Project Summary/Abstract Chronic Low Back Pain (cLBP) is a debilitating condition that affects millions of people globally. Despite increased utilization of interventions and rising medical costs, cLBP prevalence has continued to increase. This problem arises because cLBP is complex, heterogeneous and current diagnostics and treatments rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms associated with cLBP. Specifically, most diagnostics do not quantitively consider patient functional measures. To address this problem, our parent grant is focused on developing and validating a digital health platform to provide meaningful data-driven metrics that enables an integrated approach to clinical evaluation and treatment of cLBP. However, it does not directly address the operational and quality management challenges associated with performing AI/ML analyses at scale with multiple research partners. While the original grant will support some manual AI/ML analyses as part of its deliverables, additional effort is needed to achieve AI/ML readiness and maximize the full potential of the developed dataset for the BACPAC consortium and other NIH collaborators. The proposed supplementary grant will develop the needed infrastructure and pipeline to support data sharing in a more AI/ML-friendly way, while also allowing for more intimate collaborations with other consortium members who are interested in AI/ML. Technology development effort will be done in partnership with AWS Cloud services. The specific aims are to: 1) develop an AI/ML Computational data access pipeline for the Digital Health Platform; and 2) develop platform features that will streamline AI/ML workflows. The outcome of this project will decrease the overhead time spent on constantly reforming datasets so ML researchers can focus on developing models and identifying actionable findings. Collectively, this effort aligns with NIH’s strategic goals for data science and has the potential to shift clinical practice paradigms, improve patient outcomes, enhance care efficiency, and reduce costs.

项目总结/摘要 慢性腰痛（cLBP）是一种使人衰弱的疾病，影响着全球数百万人。尽管 随着干预措施的使用增加和医疗费用的上升，cLBP患病率继续增加。这 由于cLBP是复杂的、异质的，并且目前的诊断和治疗主要依赖于 主观指标，并不针对所有的多维生物心理社会机制， cLBP。具体地，大多数诊断不定量地考虑患者功能测量。为了解决这个 问题，我们的父母补助金专注于开发和验证数字健康平台， 有意义的数据驱动的指标，使临床评价和治疗的综合方法， cLBP。然而，它并没有直接解决相关的业务和质量管理挑战， 与多个研究合作伙伴进行大规模的AI/ML分析。虽然最初的赠款将支持 一些人工AI/ML分析作为其可交付成果的一部分，需要额外的努力来实现AI/ML就绪 并最大限度地发挥BACPAC联盟和其他NIH开发数据集的全部潜力 合作者拟议的追加拨款将用于发展所需的基础设施和管道， 以更友好的AI/ML方式共享数据，同时还允许与其他人进行更密切的合作 对AI/ML感兴趣的联盟成员。技术开发工作将通过伙伴关系进行 AWS云服务。具体目标是：1）开发AI/ML计算数据访问管道 数字健康平台; 2）开发平台功能，简化AI/ML工作流程。的 该项目的成果将减少花费在不断改革数据集上的开销时间， 研究人员可以专注于开发模型和确定可行的研究结果。总的来说，这一努力与 与NIH的数据科学战略目标相一致，并有可能改变临床实践范式，提高 患者的治疗效果，提高护理效率，降低成本。