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）导致大量发病率，死亡率和超过6B美元的年度住院费用 在近600万美国成年人中。 AF是一种心律不齐，可能导致血液收集 心房和形式的凝块传播到大脑，导致中风。降低与中风率相关的努力 AF受到基本中风风险分层工具和护理差异的限制。迫切需要 个性化的，社会意识，公平的中风风险预测AF患者可实现最佳 实施当代预防疗法。 该建议的目的是使用人工智能（AI）和机器学习方法来捕获和 在社会经济环境中量化已知和新发现的AF风险因素之间的协同作用。我的中央 假设是可以通过利用计算方法的方法来改善预防中风 在大型数据集上，增强了有关健康决定因素（SDOH）的信息。初步研究 我们的小组和其他人揭示了SDOH因素对准确风险至关重要的患者亚组 分层。 AIM 1是针对包括SDOH数据的AF患者发现新的风险因素关系， 使用创新的合并症发现框架（泊松二项式合并症发现）。目标2 专注于将AIM 1中确定的变量与已建立的风险因素结合的构建模型 使用AI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和新确定的因素对与AF相关的中风风险的影响。 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整个护理的手段提供手段 AF患者的频谱和解决当前护理标准中的差异。创建的AI工具将 临床医生和患者很容易访问和解释，以帮助指导个人治疗决策。 该提案的完成将产生一种个性化和公平的方法来预防中风 AF。 该项目提供了多学科计算和临床培训的增强 两个领域的专家。概述的培训将为我提供计算和翻译 作为独立研究者和医师科学家成功所需的心脏病经验。