Developing Explainable AI for Equitable Risk Stratification of Atrial Fibrillation and Stroke

开发可解释的人工智能以实现心房颤动和中风的公平风险分层

基本信息

批准号：
10752585
负责人：
Raquel Reisinger
金额：
$ 5.27万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10752585
关键词：
Address Adult Affect American Arrhythmia Artificial Intelligence Atrial Fibrillation Awareness Big Data Blood Blood coagulation Blood flow Brain Cardiology Caring Cause of Death Classification Clinical Coagulation Process Complex Computing Methodologies Confounding Factors (Epidemiology)Data Data Set Development Diagnosis Disparity Equity Etiology Health system Heart Atrium Hospital Costs Individual Knowledge Machine Learning Mathematics Mentorship Methods Modeling Morbidity - disease rate Outcome Patients Physicians Population Heterogeneity Prevalence Prevention Prevention therapy Publishing Research Personnel Risk Risk Factors Risk Management Scientist Stroke Stroke prevention Training Travel Universities Utah Work artificial intelligence method clinical risk clinical training comorbidity computerized tools disparity reduction experience falls health care disparity health data health disparity improved innovation large datasets machine learning method model building mortality multidisciplinary novel outcome prediction patient subsets risk prediction risk prediction model risk stratification social social health determinants socioeconomics standard of care stroke risk synergism therapy development tool treatment guidelines web app

项目摘要

PROJECT SUMMARY Atrial fibrillation (AF) leads to significant morbidity, mortality, and over $6B in annual hospitalization costs among the nearly 6 million US adults it affects. AF is a cardiac arrhythmia which can cause blood to collect in the atria and form clots that travel to the brain resulting in a stroke. Efforts to reduce rates of stroke related to AF are limited by rudimentary stroke risk stratification tools and disparities in care. There is a critical need for personalized, socially aware, equitable stroke risk prediction among patients with AF to enable optimal implementation of contemporary stroke-prevention therapies. The objective of this proposal is to use artificial intelligence (AI) and machine learning methods to capture and quantify synergies among known and newly discovered AF risk factors in socioeconomic contexts. My central hypothesis is that stroke prevention can be improved through methods that leverage computational methods on large datasets augmented with information on social determinants of health (SDoH). Preliminary studies by our group and others have revealed subgroups of patients for whom SDoH factors are critical for accurate risk stratification. Aim 1 is to discover new risk-factor relationships for patients with AF that include SDoH data, using an innovative comorbidity discovery framework (Poisson Binomial Comorbidity Discovery). Aim 2 focuses on building models that combine the variables identified in Aim 1 with established risk factors to predict outcomes using AI methods. To do so, I will build novel Probabilistic Graphical Models (PGMs) to understand the impact of SDoH and newly identified factors on AF-related stroke risk. The primary innovation in this proposal is employing novel analytic approaches to understand and reduce disparities in AF risk prediction models. The proposal aims to provide means for improved care across the spectrum of patients with AF and address disparities in the present standard of care. The AI tools created will be readily accessible and interpretable by clinicians and patients to help guide individual treatment decisions. Completion of this proposal will yield a personalized and equitable approach to stroke prevention in the context of AF. This project provides multidisciplinary computational and clinical training augmented with mentorship from experts in both domains. The outlined training will provide me with the computational and translational cardiology experiences required to succeed as an independent investigator and physician-scientist.

项目概要心房颤动 (AF) 会导致严重的发病率、死亡率以及每年超过 6B 美元的住院费用受其影响的近 600 万美国成年人。 AF 是一种心律失常，可导致血液聚集在体内心房并形成血栓，进入大脑导致中风。降低中风发生率的努力与房颤受到基本的中风风险分层工具和护理差异的限制。迫切需要对 AF 患者进行个性化、具有社会意识、公平的中风风险预测，以实现最佳的实施当代中风预防疗法。该提案的目标是使用人工智能（AI）和机器学习方法来捕获和量化社会经济背景下已知和新发现的房颤风险因素之间的协同作用。我的中央假设可以通过利用计算方法的方法来改善中风预防在大型数据集上补充了健康社会决定因素（SDoH）的信息。初步研究我们的团队和其他人已经揭示了 SDoH 因素对于准确风险至关重要的患者亚组分层。目标 1 是发现 AF 患者的新危险因素关系，包括 SDoH 数据、使用创新的共病发现框架（泊松二项式共病发现）。目标2 专注于构建模型，将目标 1 中确定的变量与既定风险因素相结合进行预测使用人工智能方法的结果。为此，我将构建新颖的概率图形模型（PGM）来理解 SDoH 和新发现的因素对 AF 相关中风风险的影响。该提案的主要创新在于采用新颖的分析方法来理解和减少房颤风险预测模型的差异。该提案旨在提供改善整个医疗保健的手段 AF 患者的范围并解决当前护理标准中的差异。创建的人工智能工具将临床医生和患者可以轻松获取和解释，以帮助指导个体治疗决策。该提案的完成将产生一种个性化且公平的中风预防方法 AF。该项目提供多学科计算和临床培训，并辅以来自这两个领域的专家。概述的培训将为我提供计算和翻译能力作为一名独立研究者和医师科学家取得成功所需的心脏病学经验。