Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data

张量分析提供了一种有效的方法，将大量电子健康记录（EHRs）转换为有意义和可解释的临床概念，或表型，如疾病和疾病亚型。它可以将患者聚类到子组中，并捕获多个属性之间的相互作用（例如，用于治疗疾病的特定程序），从而实现精准医疗。有效的表型分型需要大量不同样本的支持，以避免潜在的群体偏倚。一个主要的挑战是如何在多个机构中共同获得表型，同时在每个地点保护个体患者的隐私。该项目的目标是开发一个联邦张量分解框架，用于使用多站点EHR数据（PREMED）进行隐私保护、鲁棒和高效的计算表型分析。虽然针对这些目标已经开发了许多用于联邦学习的技术，但它们的协同作用还没有得到很好的研究。通信效率高的技术，如压缩，由于压缩和混淆通信，对隐私（更小的披露风险）和健壮性（更小的对抗影响）具有内在的好处。此外，联合张量分解由于其多因素结构和无监督性质而提出了独特的挑战。该项目旨在利用效率、隐私和鲁棒性之间的协同作用，并利用张量分解的多因素结构，以整体方法解决这三个相互关联的挑战。研究成果将允许机构共同执行计算表型有效和高效地使用他们的隐私保护数据。该项目包括一系列相互关联的目标，包括：(1)开发联邦张量分解的通信高效技术，如局部随机梯度下降（SGD），以降低通信频率；利用张量分解的多因素结构，采用多级压缩方法减少每轮通信；(2)利用通信高效技术固有的隐私优势，开发保护隐私的联合张量分解方法；保护隐私的输入合成方法提供了更多的通用性；(3)通过利用通信高效技术固有的鲁棒性优势，开发用于处理潜在拜占庭故障和恶意站点的鲁棒性统计聚合方法；以及基于真值推理和自适应站点评估方法的基于鲁棒学习的稀疏设置聚合方法。该项目包括使用Emory和UTHealth的真实电子病历数据进行案例研究，用于阿尔茨海默病和败血症背景下的表型发现和基于表型的预测研究。该项目还包括一系列协同活动，包括组织多位点计算表型挑战；合作边车课程的开发；大学生、女性和弱势群体的积极参与。该奖项反映了美国国家科学基金会的法定使命，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。

Tensor analysis offers an effective approach to convert massive Electronic Health Records (EHRs) into meaningful and interpretable clinical concepts, or phenotypes, such as diseases and disease subtypes. It can cluster patients into subgroups and capture the interactions between multiple attributes (e.g., specific procedures used to treat a disease), enabling precision medicine. Effective phenotyping needs to be supported by a large number of diverse samples to avoid potential population bias. A major challenge is how to derive phenotypes jointly across multiple institutions, while preserving individual patients' privacy at each site. The goal of this project is to develop a federated tensor factorization framework for Privacy-preserving, Robust, and Efficient computational phenotyping using Multisite EHR Data (PREMED). While many techniques have been developed for federated learning for each of these goals, their synergy has not been well studied. Communication-efficient techniques such as compression have an intrinsic benefit to privacy (smaller disclosure risks) and robustness (smaller adversarial impact) due to the compressed and obfuscated communication. Further, federated tensor factorization presents unique challenges due to its multi-factor structure and unsupervised nature. The project aims to exploit the synergy between efficiency, privacy, and robustness and address the three interrelated challenges with a holistic approach, while utilizing the multi-factor structure of tensor factorization. The research outcome will allow institutions to jointly perform computational phenotyping using their privacy-protected data effectively and efficiently. This project includes a set of interrelated objectives including: (1) developing communication-efficient techniques for federated tensor factorization such as local Stochastic Gradient Descent (SGD) to reduce communication frequency; and multi-level compression methods to reduce per-round communication leveraging the multi-factor structure of tensor factorization; (2) developing privacy-preserving federated tensor factorization methods by exploiting the intrinsic privacy benefit of the communication-efficient techniques; and privacy-preserving input synthesization methods that offer more versatility; and (3) developing robust statistical aggregation methods for handling potential Byzantine failures and malicious sites by utilizing the intrinsic robustness benefit of the communication-efficient techniques; and robust learning-based aggregation methods for sparse settings based on truth inference and adaptive site valuation approaches. The project includes case studies using real EHR data from Emory and UTHealth for phenotype discovery and phenotype-based predictive studies in the context of Alzheimer's Disease and Sepsis. The project also includes a set of synergistic activities including organization of multi-site computational phenotyping challenges; development of collaborative sidecar courses; and active involvement of undergraduates, women and underrepresented groups.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.