Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)

人工智能和反事实可行的终结艾滋病毒应对措施 (AI-CARE-HIV)

• 批准号: 10699171
• 负责人: Jiang Bian
• 金额: $ 73.14万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-25 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699171
• 关键词: Address; Adherence; Affect; African American population; Algorithms; Area; Artificial Intelligence; Behavioral; Black race; Calibration; Caring; Clinical; Clinical Research; Cohort Studies; Complex; Coupled; Data; Data Sources; Databases; Disparity; Electronic Health Record; Epidemiology; Expert Systems; Failure; Florida; Focus Groups; Goals; HIV; Health; Health Benefit; Health Personnel; Health Services Accessibility; High Prevalence; Immunologics; Incidence; Individual; Infrastructure; Intervention; Interview; Link; Measurement; Methodology; Methods; Minority; Modeling; Natural Language Processing; Outcome; Patients; Persons; Population; Precision Health; Protocols documentation; Provider; Public Health; Quality of life; Race; Recording of previous events; Resistance; Risk; Risk Reduction; Sampling; Standardization; Structure; Surveys; Testing; Viral Load result; Virus Diseases; Work; antiretroviral therapy; artificial intelligence method; care outcomes; citizen science; cohort; comorbidity; data integration; deep learning; deprivation; disparity reduction; electronic structure; experience; health determinants; health equity; high risk; immune reconstitution; implementation science; improved; instrument; intervention effect; meetings; multidisciplinary; novel; outcome disparities; pre-exposure prophylaxis; predictive modeling; privacy preservation; programs; prospective; public health intervention; public health relevance; racial disparity; response; social; social disparities; social health determinants; social stigma; sociodemographic factors; statistics; therapy adherence; therapy design; therapy outcome; uptake; usability

ABSTRACT Florida has the highest incidence of Human Immunodeficiency Virus (HIV) infections in the US, with marked social and racial disparities. About 40% of people with HIV in Florida do not reach undetectable viral load, and Black African Americans are the most affected. Besides well-known sociodemographic factors contributing to unfavorable outcomes and disparities, part of such remains unexplained and cannot be actioned upon. Advances in artificial intelligence (AI) and increasing availability of large real-world data (RWD) databases, e.g., electronic health records (EHRs) and administrative claims data, are ideal for developing models for precision health. However, the full capabilities of AI are still hampered by the fact that EHRs are not well integrated with other relevant data sources, containing information on social and behavioral determinants of health (SDoH), especially important for HIV care access and outcomes. Further, a strong determinant of HIV outcomes is stigma, which is not captured in structured fields of EHRs, but can be identified in clinical notes via natural language processing. In fact, many other contextual- and individual-level SDoH can be extracted from clinical narratives in EHRs. Another critical problem with AI built on RWD is that, due to inherent bias in observational data like EHRs, the AI models might identify wrong effects for interventions. Thus, alternative predictions (i.e., counterfactuals) of naïve AI systems might be mistaken, potentially leading to harm. Causal inference methods are being increasingly coupled with AI to address such bias. The overarching goal of this project is to develop “AI-CARE- HIV,” an actionable counterfactual RWD AI framework to improve HIV outcomes in Florida, in particular reducing disparity through addressing SDoH. We hypothesize that a portion of the unexplained systemic disparity can be elucidated by combining causal inference and AI models that exploit complex interactions between individual- and contextual-level SDoH. This framework can then be used to develop an unbiased (under certain assumptions), actionable model usable for planning and implementing clinical and public health interventions. We will develop the project through the OneFlorida+ Clinical Research Consortium, which collates RWD data from >16.8M Floridians, and specifically the OneFlorida+ HIV cohort (now N=71,363). Our project aims to: (1) Enhance the cohort by incorporating large-scale SDoH (9,000+) and prospectively validate new SDoH, including stigma, using NLP; (2) Create polysocial risk scores from SDoH, identify population-level causal effects of SDOH-conditioned interventions on to HIV outcomes, and develop individualized counterfactual AI models for HIV outcomes, calibrated to reduce disparity; (3) Plan –with healthcare providers, State officials, citizen scientists– targeted clinical and public health interventions anchored on our counterfactual AI models, using implementation science, standardized protocols (e.g., CONSORT-AI). Our team includes multidisciplinary (methodological, clinical, qualitative) expertise supported by OneFlorida+, Fl Dept of Health, and minority- serving entities. We expect impact at multiple levels, from infrastructure enhancement to public health benefit.

抽象的 佛罗里达州是美国人类免疫缺陷病毒 (HIV) 感染发病率最高的州， 佛罗里达州约 40% 的艾滋病毒感染者没有达到不可检测的病毒载量，并且 除了众所周知的社会人口因素外，非裔美国人受影响最大。 不利的结果和差异，其中部分仍无法解释，无法采取行动。 人工智能（AI）和增加大型现实世界数据（RWD）数据库的可用性，例如电子 健康记录 (EHR) 和行政索赔数据是开发精准健康模型的理想选择。 然而，人工智能的全部功能仍然受到电子病历与其他系统没有很好集成的阻碍。 相关数据源，包含有关健康的社会和行为决定因素（SDoH）的信息，特别是 此外，艾滋病毒治疗结果的一个重要决定因素是耻辱感。 未在电子病历的结构化字段中捕获，但可以通过自然语言处理在临床记录中识别。 事实上，许多其他背景和个人层面的 SDoH 可以从 EHR 的临床叙述中提取。 基于 RWD 的人工智能的另一个关键问题是，由于 EHR 等观察数据的固有偏差， 人工智能模型可能会识别出干预措施的错误效果，从而产生替代预测（即反事实）。 幼稚的人工智能系统可能会出错，从而可能导致因果推理方法受到损害。 越来越多地与人工智能结合来解决这种偏见该项目的总体目标是开发“AI-CARE-”。 HIV”，一个可操作的反事实 RWD AI 框架，旨在改善佛罗里达州的艾滋病毒结果，特别是 通过解决 SDoH 问题来减少差距。 可以通过结合因果推理和利用复杂交互的人工智能模型来阐明差异 然后，该框架可用于开发一个公正的（根据） 某些假设），可用于规划和实施临床和公共卫生的可行模型 我们将通过 OneFlorida+ 临床研究联盟开发该项目，该联盟负责整理。 RWD 数据来自超过 1680 万佛罗里达人，特别是 OneFlorida+ HIV 队列（现在 N=71,363）。 旨在：(1) 通过纳入大规模 SDoH（9,000+）来增强队列并前瞻性地验证新的 (2) 从 SDoH 创建多社会风险评分，确定人群层面的因果关系 SDOH 条件干预措施对 HIV 结果的影响，并开发个性化的反事实人工智能 艾滋病毒结果模型，经过校准以减少差异；(3) 与医疗保健提供者、国家官员一起制定计划； 公民科学家——基于我们的反事实人工智能模型的有针对性的临床和公共卫生干预措施， 使用实施科学、标准化协议（例如 CONSORT-AI）。我们的团队包括多学科人员。 由 OneFlorida+、佛罗里达州卫生部和少数群体支持的（方法学、临床、定性）专业知识 我们预计会在多个层面产生影响，从基础设施增强到公共卫生效益。