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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.

项目总结

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）

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.