SCH: INT: Collaborative Research: Privacy-Preserving Federated Transfer Learning for Early Acute Kidney Injury Risk Prediction

联合学习使医院能够在确保患者隐私的同时协作学习共享的全球模型；然而，由于EHR的异质性，我们的应用面临着巨大的统计挑战，即患者特征和临床观察或特征空间的差异。因此，来自不同医院的真实EHR数据永远不会独立和相同地分布(IID)。这项拟议的研究旨在克服这一统计挑战，同时通过利用大型集成EHR数据集来提高联合学习的安全性，该数据集包含来自美国9个州12个医疗系统的2100多万名患者的医疗记录。提出了一种新的隐私保护联合迁移学习框架，用于构建稳健和准确的AKI预测模型，该模型需要从孤立的医疗系统中的真实EHR数据进行学习。该项目将(1)开发新的迁移学习解决方案，以应对三种不同的非IID EHR数据分析场景；(2)开发具有动态加权聚合机制的新型联邦学习框架，以构建稳健而准确的急性肾损伤(AKI)预测模型；以及(3)开发综合隐私保护的联邦迁移学习框架，其具有新颖的隐私保护解决方案，以应对拟议迁移学习应用中的独特隐私挑战。该项目提出了新的迁移学习解决方案，以应对联合学习中的非IID挑战，以及为同质和异质迁移学习任务量身定制的新的安全构建块。该项目将共同开发一个隐私保护的联邦迁移学习框架，为非IID临床数据场景提供第一个实用的解决方案。我们的研究方法和发现将为医疗保健领域的机器学习提供有前景的新方向，并将为学术研究和潜在的商业化产品做出贡献。更重要的是，在拟议的联合转移学习框架中，基础梯度增强机器模型的可解释性将提供更好的理解，临床医生可以根据这些预测因素为高危患者设计预防和管理策略。该项目由智能互联健康和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Federated learning enables hospitals to collaboratively learn a shared global model while ensuring patient privacy; however, there is a big statistical challenge for our application owing to EHR heterogeneities, i.e. difference in patient characteristics and clinical observations made or feature space. Thus, real-world EHR data from different hospitals are never independently and identically distributed (IID). The proposed research is to overcome this statistical challenge while improving security for federated learning byleveraging a large integrated EHR dataset with medical records for more than 21 million patients from 12 healthcare systems spanning across 9 US states. A novel privacy-preserving federated transfer learning framework is proposed for building a robust and accurate AKI prediction model that require learning on real-world EHR data from siloed healthcare systems. This project will (1) develop novel transfer learning solutions to address three distinct non-IID EHR data analytic scenarios, (2) develop a novel federated learning framework with a dynamic weighting aggregation mechanism to build a robust and accurate Acute kidney injury (AKI) prediction model; and (3) develop a comprehensive privacy-preserving federated transfer learning framework with novel privacy-preserving solutions to address the unique privacy challenges in the proposed transfer learning applications.The project proposes new transfer learning solutions to combat the non-IID challenge in federated learning and new security building blocks tailored for homogeneous and heterogeneous transfer learning tasks. Together the project will develop a privacy-preserving federated transfer learning framework to provide a first practical solution for non-IID clinical data scenarios. Our research methods and findings will provide promising new directions to machine learning for healthcare and will contribute to both academic research and potential commercialized products. More importantly, the interpretable nature of the base gradient boosting machine model in the proposed federated transfer learning framework will provide better understanding of the predictors from which clinicians can use to design prevention and management strategies for high-risk patients.This project is jointly funded by Smart and Connected Health and the Established Program to Stimulate Competitive Research (EPSCoR).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.