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

• 批准号: 10724442
• 负责人: Mattina Ashley Davenport
• 金额: $ 15.93万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724442
• 关键词: Address; Adolescent; Age; Aggregation Bias; Area; Artificial Intelligence; Behavior; Caring; Characteristics; Child health care; Childhood; Classification; Clinical; Data; Data Set; Databases; Detection; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease Management; Disparity; Disparity population; Drowsiness; Electronic Health Record; Ensure; Equity; Ethnic Origin; Future; Gender; Geography; Goals; Health Sciences; Health system; Knowledge; Learning; Machine Learning; Measurement; Medicine; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Modeling; Napping; National Heart, Lung, and Blood Institute; National Institute on Minority Health and Health Disparities; Parents; Patients; Pediatric Hospitals; Performance; Phenotype; Population; Predisposition; Prevalence; Process; Public Health; Race; Reporting; Research; Research Personnel; Resources; School-Age Population; Screening procedure; Sensitivity and Specificity; Services; Sleep; Sleep Deprivation; Sleep Disorders; Sleep disturbances; Socioeconomic Status; Source; Structure; Subgroup; Technology; Testing; Training; Translating; Underserved Population; United States National Institutes of Health; Validation; Work; Writing; alertness; biomedical informatics; career; career development; classification algorithm; cohort; computable phenotypes; cost effective; data sharing networks; design; disparity gap; electronic data; experience; health care settings; health disparity; health equity; improved; improvement on sleep; innovation; interest; learning network; marginalization; medical specialties; member; minority children; novel; patient subsets; poor sleep; population health; racial minority; responsible research conduct; satisfaction; screening; screening disparities; skills; sleep health; tool; underserved community

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.

项目摘要 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民族中是突出的， 睡眠持续时间、时间、警觉性、行为和质量/障碍的维度。尽管睡眠的证据 缺乏负担的少数民族青年，这些易感亚群是认识不足，在临床 工作流程导致睡眠医学专业服务。忽视这种潜在的偏见， 临床环境中的儿科睡眠队列（例如，历史上白色患者比例过高）。可计算 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）。为儿童睡眠不足开发一种可计算的表型将有助于 让我们把睡眠筛查和护理的目标放在最需要的地方。然而，要做到这一点，我们必须确保 可计算表型被设计为捕获传统上遗漏的组，并且不以 伤害了历史上被边缘化的亚群体。本K 01将通过确定潜在的 EHR数据集固有的偏见，了解其原因，并使用严格的方法减轻它们。的 拟议的K 01奖将允许我进行以下目标：1）开发和验证一个 可计算表型算法用于儿童睡眠不足分类;和2）后处理的应用 偏差缓解方法，以建立和测试公平的可计算表型模型。我的主要目标是 成为一名独立的调查员，专注于检测儿童睡眠不足，并将其转化为 知识转化为有效的策略，以改善服务不足社区的睡眠健康。实现这一目标 需要在特定内容领域的培训和研究指导，以（1）学习先进的生物医学信息学 利用EHR的方法（例如，可计算表型），并开发自动筛选工具， 儿科卫生系统的使用，（2）发展人口水平睡眠差异研究和SDH的专业知识 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)and applying应用machine机learning学习.我提出的研究和培训计划将使我具备 成为儿科睡眠研究和人口健康科学的独立研究者， 准备在跨学科的临床和技术团队中工作。一支杰出的跨学科团队 他们聚集在一起，以完成这项K 01研究的目标，并在对我至关重要的培训领域指导我。 长期职业发展。我的K 01导师团队包括职业生涯中期（Azizi Seixas博士、Jennifer 库珀，克里斯托弗·巴特利特）和高级导师/合作者（Deena Chisolm博士，吕红芳博士，凯利 凯莱赫，劳伦黑尔），确保我有机会接触研究人员利用最新的尖端方法， 以及拥有庞大合作网络和资源的导师，帮助我开展职业生涯。