Development and Validation of an Equitable Computable Phenotype for Classifying Pediatric Sleep Deficiency in Electronic Health Records

开发和验证电子健康记录中儿童睡眠不足分类的公平可计算表型

基本信息

项目摘要

PROJECT SUMMARY Sleep deficiency remains one of the most prominent and unaddressed public health concerns in pediatric healthcare settings. Pediatric sleep disparities are prominent across minoritized racial subpopulations in the dimensions of sleep duration, timing, alertness, behaviors, and quality/disorders. Despite the evidence of sleep deficiency burdening minoritized youth, these susceptible subpopulations are underrecognized in the clinical workflow leading to sleep medicine specialty services. Ignoring this underlying bias has yielded poorly defined pediatric sleep cohorts in clinical contexts (e.g., historical overrepresentation of White patients). A computable phenotype offers an efficient way to examine a large amount of data from many health systems, specifically electronic health record (EHR) data. Developing a computable phenotype for pediatric sleep deficiency will help us to target sleep screening and care where it is needed the most. However, to do this we will have to ensure the computable phenotype is designed to capture traditionally missed groups and is not biased in a way which harms historically marginalized subpopulations. This K01 will address these equity gaps by identifying potential biases inherent in EHR datasets, understanding their causes, and mitigating them using rigorous methods. The proposed K01 award will allow me to conduct the following aims: 1) the development and validation of a computable phenotype algorithm for classifying pediatric sleep deficiency; and 2) application of postprocessing bias mitigation methods to build and test an equitable computable phenotype model. My primary goal is to become an independent investigator focused on detecting pediatric sleep deficiency and translating that knowledge into effective strategies to improve sleep health in underserved communities. Achieving this goal requires training and research mentorship in specific content areas to (1) learn advanced biomedical informatics approaches for leveraging EHR (e.g., computable phenotyping) and develop an automated screening tool for use by pediatric health systems, (2) develop expertise in population-level sleep disparities research and SDH measurement, and (3) employ responsible conduct of research skills in developing unbiased artificial intelligence (AI) and applying machine learning. My proposed research and training plan will equip me with the skills necessary to become an independent investigator in pediatric sleep research and population health science, prepared to work in interdisciplinary clinical and technical teams. An exceptional interdisciplinary team has been assembled to complete the aims of this K01 research, as well as to mentor me in the training areas critical to my long-term career development. My K01 mentorship team includes both mid-career (Drs. Azizi Seixas, Jennifer Cooper, Christopher Bartlett) and senior mentors/collaborators (Drs. Deena Chisolm, Hongfang Lui, Kelly Kelleher, Lauren Hale), ensuring that I have access to researchers utilizing the latest cutting-edge methods, as well as mentors with large collaborative networks and resources to help launch my career.
项目概要 睡眠不足仍然是儿科最突出和未解决的公共卫生问题之一 医疗保健设置。儿童睡眠差异在少数种族亚群中很突出 睡眠持续时间、时间、警觉性、行为和质量/障碍的维度。尽管有睡眠的证据 缺乏给少数青少年带来负担,这些易感亚群在临床上未被充分认识 提供睡眠医学专业服务的工作流程。忽视这种潜在的偏见已经产生了不明确的定义 临床背景下的儿科睡眠队列(例如,历史上白人患者的比例过高)。一个可计算的 表型提供了一种有效的方法来检查来自许多卫生系统的大量数据,特别是 电子健康记录 (EHR) 数据。开发针对儿科睡眠不足的可计算表型将有所帮助 我们将睡眠筛查和护理作为最需要的地方。然而,要做到这一点,我们必须确保 可计算的表型旨在捕获传统上遗漏的群体,并且不会产生偏见 伤害历史上被边缘化的亚人群。 K01 将通过识别潜力来解决这些股权差距 电子病历数据集中固有的偏差,了解其原因,并使用严格的方法减轻它们。这 拟议的 K01 奖项将使我能够实现以下目标:1)开发和验证 用于对儿科睡眠不足进行分类的可计算表型算法; 2)后处理的应用 偏差缓解方法来构建和测试公平的可计算表型模型。我的首要目标是 成为一名独立调查员,专注于检测儿科睡眠不足并将其转化为 将知识转化为改善服务欠缺社区睡眠健康的有效策略。实现这一目标 需要特定内容领域的培训和研究指导,以 (1) 学习高级生物医学信息学 利用 EHR 的方法(例如,可计算表型分析)并开发自动筛选工具 儿科卫生系统使用,(2) 发展人口水平睡眠差异研究和 SDH 方面的专业知识 测量,以及(3)采用负责任的研究技能来开发公正的人工智能 (人工智能)和应用机器学习。我提出的研究和培训计划将使我具备这些技能 有必要成为儿科睡眠研究和人口健康科学的独立研究者, 准备在跨学科临床和技术团队中工作。一支杰出的跨学科团队 聚集在一起完成这项 K01 研究的目标,并在对我至关重要的培训领域提供指导 长期的职业发展。我的 K01 导师团队包括职业中期人员(Azizi Seixas 博士、Jennifer Cooper、Christopher Bartlett)和高级导师/合作者(Deena Chisolm 博士、Hongfang Lui、Kelly Kelleher、Lauren Hale),确保我能够接触到利用最新尖端方法的研究人员,例如 以及拥有大型协作网络和资源的导师来帮助我开启职业生涯。

项目成果

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