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

• 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
• 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

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.

项目摘要 房颤（AF）导致显著的发病率、死亡率和每年超过60亿美元的住院费用 在近600万美国成年人中，AF是一种心律失常，可导致血液聚集在 心房并形成血栓，血栓进入大脑导致中风。努力降低与以下因素相关的中风发生率 房颤受到基本卒中风险分层工具和护理差异的限制。迫切需要掌握 在AF患者中进行个性化、社会意识、公平的卒中风险预测， 现代中风预防疗法的实施。 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AF风险因素之间的协同作用。我的中枢 一种假设是，可以通过利用计算方法的方法来改善中风预防 在大型数据集上增加了健康的社会决定因素（SDoH）的信息。初步研究由 我们的研究小组和其他研究小组已经揭示了SDoH因素对准确风险至关重要的患者亚组 分层目的1是发现AF患者的新风险因素关系，包括SDoH数据， 使用创新的comormonism发现框架（泊松二项式comormonism发现）。目的2 重点是建立模型，将目标1中确定的变量与既定的风险因素相结合， 使用AI方法。为此，我将构建新颖的概率图形模型（PGMs）来理解 SDoH和新发现的因素对AF相关卒中风险的影响。 该提案的主要创新是采用新颖的分析方法来理解和减少 AF风险预测模型的差异。该提案旨在为改善整个国家的护理提供手段。 房颤患者的范围，并解决目前标准治疗的差异。创建的AI工具将 临床医生和患者可以随时访问和解释，以帮助指导个人治疗决策。 完成这一建议将产生一个个性化的和公平的方法来预防中风的背景下， 的AF。 该项目提供多学科的计算和临床培训， 这两个领域的专家。概述的培训将为我提供计算和翻译 作为一名独立的研究者和医生-科学家所需的心脏病学经验。