Robust Diagnosis in Electronic Health Records Integrating Physics-based Missing Data Multiple Imputation, Fast Inference for Hemodynamic Models, and Differential Privacy.

该项目将引入处理电子健康记录（EHR）数据中缺失值的新范式，目的是开发早期诊断舒张期心室功能障碍的新方法，舒张期心室功能障碍是一种无声疾病，占全球心力衰竭相关死亡总数的三分之一。电子病历往往杂乱无章，存在各种原因导致的数据缺失问题，如出现某种疾病的首发症状后临床检查频繁，常规筛查时检查频率较低。数据缺失往往限制了从这些来源提取有用信息的能力（例如，早期诊断）。该项目的目标是利用缺失信息有时满足数学或物理原理的事实，开发创新的基于模型的imputation方法，在大型电子病历数据集中结合模型和有效的隐私保护学习技术。将开发计算效率高的算法来训练数值模型，同时保护患者隐私，这些方法的可行性和实用性将在文献中尚未解决的规模上得到证明。为本项目开发的预测数值模型方法可广泛应用于各个领域。其他项目目标包括通过免费提供的开源软件库开发用于研究和教育的基础设施。该项目还将为本科生和研究生提供宝贵的多学科技能。研究和外联工作的重点都是增加妇女、残疾人和代表性不足群体的参与。该团队将通过数值模型开发新的正则化方法，即，经过最佳训练的模型能够根据底层物理提出缺失数据的分布。对于表征心血管功能的电子病历，集总参数血流动力学模型提供了理想的正则化器。使用马尔可夫链蒙特卡罗对这些模型进行参数估计计算成本高，因此与快速应用于大型电子病历集合不兼容。此外，心血管系统的最佳训练数值模型可以被认为是一种查询类型，引起患者隐私问题。提出的研究通过以下方式解决这些问题：(1)获取和分析大型心力衰竭电子病历数据集。(2)基于同伦优化的血流动力学模型隐私保护变分推理的发展。(3)结合不确定性量化和数值模型，实施和广泛测试新的缺失数据imputation方法。(4)大患者队列的论证。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。

This project will introduce new paradigms for dealing with missing values in electronic health record (EHR) data, with the objective of developing novel approaches for early diagnosis of diastolic ventricular dysfunction, a silent disease responsible for one-third of the total heart failure-related deaths worldwide. EHR are often messy and suffer from missing data problem for various reasons, for example more frequent clinical exams after the manifestation of the first symptoms of a certain disease and less frequent exams during routine screening. Missing data often limits the ability to extract useful information from these sources (e.g., early diagnosis). The goal of this project is to leverage the fact that missing information sometimes satisfies mathematical or physical principles to develop innovative model-based imputation approaches, combining models and efficient privacy-preserving learning techniques in large EHR datasets. Computationally efficient algorithms will be developed to train numerical models while preserving patient privacy, and the feasibility and practical usefulness of these approaches will be demonstrated at a scale that has not yet been addressed in the literature. The approaches for predictive numerical models developed for this project can be applied broadly in various fields. Additional project goals include development of infrastructure for research and education through freely available, open-source software libraries. This project will also provide invaluable multi-disciplinary skills to undergraduate and graduate students. Both research and outreach efforts focus on increasing the participation of women, people with disabilities, and of underrepresented groups.The team will develop novel regularization approaches through numerical models, i.e., optimally trained models able to suggest distributions of missing data based on the underlying physics. For EHRs characterizing cardiovascular function, lumped parameter hemodynamic models offer an ideal regularizer. Parameter estimation for these models using Markov chain Monte Carlo is computationally expensive and therefore incompatible with fast application to large EHR collections. Additionally, optimally trained numerical models of the cardiovascular system can be thought as a type of query, rising issues of patient privacy. The proposed research tackles these issues through: (1) Acquisition and analysis of a large heart failure EHR dataset. (2) Development of privacy-preserving variational inference for hemodynamic models, enhanced using homotopy-based optimization. (3) Implementation and extensive testing of novel imputation approaches for missing data, combining uncertainty quantification and numerical models. (4) Demonstration on a large patient cohort.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.