SBIR Phase I: Artificial intelligence platform for secure, collaborative learning across medical institutions

SBIR 第一阶段：用于跨医疗机构安全协作学习的人工智能平台

• 批准号: 2136775
• 负责人: Bharat Rao
• 金额: $ 25.6万
• 依托单位: SOAR ANALYTICS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136775&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; intelligence; platform

The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a collaborative-learning platform that generates accurate population health models from secure access to patient records. The proposed system will overcome privacy concerns to enable AI methods to securely access hundreds of millions of patient records from multiple institutions across the US to learn high-performing predictive models. Models learned from this platform are differentiated due to their training data and help payors, providers, and pharma companies that benefit from early diagnosis and treatment of patients that may have remained undiagnosed. This system will improve patient outcomes and health care system performance. This Small Business Innovation Research (SBIR) Phase I project will address fundamental limitations that can deter medical institutions from sharing patient data to support learning clinical-grade models. Unlike federated learning (FL), neither local data nor local model parameters are shared; rather, local classifiers predict labels for an unlabeled global dataset. Sharing model parameters in FL can violate privacy requirements and expose patient data used for local training. This novel platform is designed to be immune to the “white box” attacks of FL, and its main privacy risk for membership inference is significantly lower. This platform will accurately include information from all subpopulations and will support collaborative learning for multiple ML algorithms, including human-interpretable algorithms that cannot be learned with FL. Accuracy will be evaluated via sensitivity and specificity, privacy via membership vulnerability. Methods compared include federated learning and differential privacy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是开发一个协作学习平台，通过安全访问患者记录来生成准确的人口健康模型。 拟议的系统将克服隐私问题，使人工智能方法能够安全地访问来自美国多家机构的数亿份患者记录，以学习高性能的预测模型。从这个平台学习的模型由于其训练数据而有所不同，并帮助支付者，提供者和制药公司从早期诊断和治疗可能尚未诊断的患者中受益。该系统将改善患者的治疗效果和医疗保健系统的绩效。这个小型企业创新研究（SBIR）第一阶段项目将解决阻碍医疗机构共享患者数据以支持学习临床级模型的基本限制。与联邦学习（FL）不同，本地数据和本地模型参数都不共享;相反，本地分类器为未标记的全局数据集预测标签。在FL中共享模型参数可能会违反隐私要求，并暴露用于本地训练的患者数据。这个新颖的平台被设计成免疫于FL的“白色盒子”攻击，并且其成员推断的主要隐私风险显著降低。该平台将准确地包含来自所有亚群的信息，并将支持多种ML算法的协作学习，包括无法使用FL学习的人类可解释算法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。