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

电子健康记录中的稳健诊断集成了基于物理的缺失数据多重插补、血流动力学模型的快速推理和差分隐私。

• 批准号: 1918692
• 负责人: Daniele Schiavazzi
• 金额: $ 88.02万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918692&HistoricalAwards=false
• 关键词: Robust; Diagnosis; Electronic; Health; Records

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.

该项目将引入新的范例来处理电子健康记录（EHR）数据中的缺失值，目的是开发新的方法来早期诊断舒张性心室功能障碍，这是一种沉默的疾病，占全球心力衰竭相关死亡总数的三分之一。电子健康记录通常是混乱的，并且由于各种原因而遭受数据缺失问题，例如在某种疾病的第一个症状出现后进行更频繁的临床检查，而在常规筛查期间进行的检查较少。缺失数据通常限制了从这些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插补方法，在大型EHR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，并且这些方法的可行性和实际有用性将以文献中尚未讨论的规模进行证明。为该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研究和教育基础设施。该项目还将为本科生和研究生提供宝贵的多学科技能。研究和推广工作都集中在增加妇女、残疾人和代表性不足的群体的参与。该团队将通过数值模型开发新的正规化方法，即，经过优化训练的模型能够根据基础物理学建议缺失数据的分布。对于表征心血管功能的EHR，集总参数血流动力学模型提供了理想的正则化器。这些模型的参数估计使用马尔可夫链蒙特卡罗计算是昂贵的，因此不兼容的快速应用程序，以大型EHR集合。此外，经过最佳训练的心血管系统数值模型可以被认为是一种查询，从而引发了患者隐私问题。本研究主要通过以下几个方面来解决这些问题：（1）获取和分析一个大型的心力衰竭EHR数据集。(2)开发血液动力学模型的隐私保护变分推理，使用基于同伦的优化进行增强。(3)实施并广泛测试缺失数据的新型插补方法，结合不确定性量化和数值模型。(4)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。

项目成果

AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation

• DOI: 10.1615/int.j.uncertaintyquantification.2022043110
• 发表时间: 2022-02
• 期刊: ArXiv
• 作者: Emma R. Cobian;J. Hauenstein;Fang Liu;D. Schiavazzi

Multifidelity data fusion in convolutional encoder/decoder networks

• DOI: 10.1016/j.jcp.2022.111666
• 发表时间: 2022-04
• 期刊: ArXiv
• 作者: Lauren Partin;G. Geraci;A. Rushdi;M. Eldred;D. Schiavazzi

Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models

使用 NoFAS 进行变分推理：使用自适应代理对计算成本较高的模型进行流标准化

• DOI: 10.48550/arxiv.2108.12657
• 发表时间: 2022
• 期刊: Journal of computational physics
• 影响因子: 4.1
• 作者: Wang, Yu;Liu, Fang;Schiavazzi, Daniele E.
• 通讯作者: Schiavazzi, Daniele E.

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

左心室舒张功能不全继发性肺动脉高压的预测模型

• DOI: 10.1101/2020.04.23.20073601
• 发表时间: 2021
• 期刊: Frontiers in physiology
• 影响因子: 4
• 作者: Harrod, Karlyn K.;Rogers, Jeffrey L.;Feinstein, Jeffrey A.;Marsden, Alison L.;Schiavazzi, Daniele E.
• 通讯作者: Schiavazzi, Daniele E.