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

项目总结 睡眠不足仍然是儿科患者中最突出和尚未解决的公共卫生问题之一 医疗保健设置。儿科睡眠不平等在全国小规模种族亚群中十分突出 睡眠持续时间、时间、警觉性、行为和质量/紊乱的维度。尽管有睡眠的证据 缺乏负担的矿化青年，这些易感亚群在临床上被低估了 工作流程通向睡眠医学专科服务。忽视这种潜在的偏见产生了模糊的定义 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)。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。为儿童睡眠不足开发一种可计算的表型将有所帮助 我们要有针对性地进行睡眠筛查，并在最需要的地方进行护理。然而，要做到这一点，我们必须确保 可计算的表型旨在捕捉传统上遗漏的群体，并且不存在偏见 伤害了历史上被边缘化的亚群体。此K01将通过确定潜在客户数量来解决这些股权缺口 EHR数据集中固有的偏差，了解其原因，并使用严格的方法缓解这些偏差。这个 建议的K01奖将使我能够实现以下目标：1)开发和验证 儿科睡眠不足分类的可计算表型算法；2)后处理的应用 建立和测试公平的可计算表型模型的偏差缓解方法。我的首要目标是 成为一名独立的调查员，专注于检测儿科睡眠不足并将其 将知识转化为有效的战略，以改善服务不足社区的睡眠健康。实现这一目标 需要特定内容领域的培训和研究指导(1)学习高级生物医学信息学 利用EHR的方法(例如，可计算的表型分析)并开发自动筛查工具 供儿科保健系统使用，(2)发展人口层面睡眠差距研究和SDH方面的专门知识 测量，以及(3)在开发无偏见的人工智能时采用负责任的研究技能 (AI)和应用机器学习。我提议的研究和培训计划将使我具备以下技能 成为儿科睡眠研究和人口健康科学的独立研究员， 准备在跨学科的临床和技术团队中工作。一支杰出的跨学科团队 完成这项K01研究的目标，并在对我至关重要的培训领域指导我 长期的职业发展。我的K01导师团队包括职业生涯中期(Azizi Seixas博士、Jennifer博士 库珀、克里斯托弗·巴特利特)和高级导师/合作者(Deena Chsolm博士、雷红芳博士、Kelly Kelleher，Lauren Hale)，确保我可以接触到使用最新尖端方法的研究人员，如 以及拥有大型协作网络和资源的导师，帮助我开始职业生涯。

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.