Deep probabilistic predictive models for stroke and coronary heart disease

中风和冠心病的深度概率预测模型

基本信息

批准号：
10678650
负责人：
Rajesh Ranganath
金额：
$ 64.05万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10678650
关键词：
Address Affect Age Algorithms Area Brain hemorrhage Cardiovascular Diseases Cardiovascular system Caring Cause of Death Cessation of life Clinical Clinical Data Collection Coronary heart disease Data Data Sources Development Diagnostic Direct Costs Electronic Health Record Equity Ethnic Origin Event Future Health Healthcare Hemorrhage Hospitals Human Image Individual Inequity Intervention Knowledge Learning Machine Learning Manuals Mathematics Measurement Medical Medicine Methods Minority Groups Modeling Morbidity - disease rate Natural Language Processing Nature Neurologic Patients Pattern Persons Physicians Population Prevention Productivity Publications Race Research Risk Risk Assessment Risk Estimate Risk Factors Series Source Code Statistical Models Stroke Survival Analysis Techniques Technology Text Time Uncertainty United States Vision Work Writing aggressive therapy burden of illness cardiovascular risk factor clinical conference clinical practice clinical risk cost deep learning deep learning model design disease prognosis disorder risk effectiveness evaluation electronic health information ethnic bias ethnic diversity experience flexibility health data heart disease risk heterogenous data high dimensionality high risk improved learning strategy minority communities mortality natural language negative affect neural neural network next generation open source personalized risk prediction point of care predictive modeling prevent racial bias racial diversity risk prediction stroke model stroke risk thromboembolic stroke thrombotic

项目摘要

Project Summary Cardiovascular disease negatively affects millions of people worldwide. Globally, it accounts for approximately thirty percent of all deaths. Furthermore, a significant fraction of deaths caused by cardiovascular disease occur in a non-geriatric population; fifteen percent of all worldwide deaths are attributed to cardiovascular disease for people under the age of seventy. Treatment to prevent cardiovascular events should be based on highly individualized risk prediction. High risk patients should get more aggressive treatments because the risk of disease outweighs the burden of treatment, while low risk patients should be managed more conservatively. For example, anti-thrombotic therapy for coronary heart disease may increase bleeding risk and may not be appropriate for low-risk patients. Two primary kinds of cardiovascular disease are stroke and coronary heart disease, and there have been a number of developments in risk scores for both ailments. However, these risk scores only use a small fraction of the available measurements about a patient and treat risk as a collection of independent factors rather than considering how their interactions amplify or ameliorate risk. Moreover, a majority of the popular coronary heart disease and stroke risk scores are designed to be manually computed by a busy physician at the point of care, which further limits their scope and fidelity. Next generation risk scores for stroke and cardiovascular disease should take into account all of the available information in the electronic health record without the constraints of the parametric assumptions of traditional risk modeling. More accurate risk assessment of coronary heart disease and stroke will lead to better care and reduce the cardiovascular disease burden. Our vision is to capitalize on large collections of electronic health records along with recent advances in deep learning to build risk scores that use more available health information while making minimal mathematical assumptions about the nature of clinical risk. Our proposal propels the field from human computable independent risks calculations necessitated by previous limitations of technology to calculations that make use of deep learning to learn highly nonlinear risks and risk factor interactions. We additionally demonstrate how deep learning can be used to deal with the ever-present issue of missing values in medicine. Our proposal also targets an area under- explored by previous work on risk scores: fairness. Treatment quality is affected by the quality of risk estimation. This means populations where estimated risk is less accurate may receive worse care. Risk scores developed with simple models may only capture risk accurately for the majority population as simple models are not flexible enough to cover multiple populations. We seek to identify potential risk calculation differences with respect to race and ethnicity. We will construct and evaluate deep learning methods for coronary heart disease and stroke risk assessment from electronic health records. We will develop techniques to incorporate clinical text, handle missing data, and evaluate fairness of deep learning for cardiovascular risk scores. Finally, we will make our work available as open source code written in deep learning frameworks, at clinical conferences, and publications.

项目概要心血管疾病对全世界数百万人产生负面影响。在全球范围内，它约占占所有死亡人数的百分之三十。此外，很大一部分死亡是由心血管疾病引起的在非老年人群中；全球死亡人数的 15% 归因于心血管疾病对于七十岁以下的人。预防心血管事件的治疗应基于高度个性化风险预测。高风险患者应该接受更积极的治疗，因为疾病超过了治疗负担，而低风险患者应采取更保守的治疗。例如，冠心病的抗血栓治疗可能会增加出血风险，但可能不会适合低危患者。两种主要的心血管疾病是中风和冠心病疾病，并且这两种疾病的风险评分都取得了许多进展。然而，这些风险评分仅使用有关患者的可用测量值的一小部分，并将风险视为风险的集合独立因素，而不是考虑它们的相互作用如何放大或减轻风险。此外，大多数流行的冠心病和中风风险评分被设计为由忙碌的人手动计算医生在护理点，这进一步限制了他们的范围和保真度。下一代中风风险评分和心血管疾病应考虑电子健康记录中的所有可用信息不受传统风险建模参数假设的约束。更准确的风险评估冠心病和中风的预防将带来更好的护理并减轻心血管疾病的负担。我们的愿景是利用大量电子健康记录以及最新的进展深度学习建立风险评分，使用更多可用的健康信息，同时进行最少的数学计算关于临床风险性质的假设。我们的提案推动了人类可计算独立领域的发展由于之前的技术限制，利用深度学习的计算需要进行风险计算学习高度非线性的风险和风险因素的相互作用。我们还展示了深度学习如何用于解决医学中始终存在的价值缺失问题。我们的建议还针对以下领域：之前关于风险评分的工作探讨了：公平性。治疗质量受到风险评估质量的影响。这意味着估计风险不太准确的人群可能会得到更差的护理。制定风险评分使用简单模型可能只能准确捕捉大多数人群的风险，因为简单模型不灵活足以覆盖多个人群。我们寻求识别潜在的风险计算差异种族和民族。我们将构建和评估冠心病和中风的深度学习方法电子健康记录的风险评估。我们将开发技术来整合临床文本、处理缺失数据，并评估深度学习对心血管风险评分的公平性。最后，我们将完成我们的工作以深度学习框架编写的开源代码的形式在临床会议和出版物上提供。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Causal Estimation with Functional Confounders.

具有功能混杂因素的因果估计。

DOI：
发表时间：
2020
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Puli,Aahlad;Perotte,AdlerJ;Ranganath,Rajesh
通讯作者：
Ranganath,Rajesh

Learning Invariant Representations with Missing Data.

学习缺失数据的不变表示。

DOI：
发表时间：
2022
期刊：
Proceedings of machine learning research
影响因子：
0
作者：
Goldstein,Mark;Puli,Aahlad;Ranganath,Rajesh;Jacobsen,Jörn-Henrik;Chau,Olina;Saporta,Adriel;Miller,AndrewC
通讯作者：
Miller,AndrewC

Deep learning models for electrocardiograms are susceptible to adversarial attack.