Electronic Health Record Phenotyping for Case Detection and Prediction of Emergency Department Visits for Child and Adolescent Suicide Attempts

用于病例检测和预测儿童和青少年自杀未遂急诊科就诊的电子健康记录表型

• 批准号: 10507372
• 负责人: Juliet Beni Edgcomb
• 金额: $ 19.74万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10507372
• 关键词: 10 year old; 17 year old; Achievement; Address; Adolescent; Adolescent Psychiatry; Advocate; Age; Algorithms; Area; California; Caring; Cause of Death; Characteristics; Child; Child Psychiatry; Classification; Clinical; Clinical Data; Clinical Informatics; Code; Complement; Computer software; Computing Methodologies; Data; Data Set; Decision Making; Detection; Development; Diagnosis; Diagnostic; Discipline; Disease; Early Diagnosis; Electronic Health Record; Emergency Care; Emergency department visit; Ethnic Origin; Evidence based intervention; Face; Family; Family member; Future; Goals; Health Care Visit; Health system; Individual; Informatics; Insurance; International; Knowledge; Lasso; Los Angeles; Machine Learning; Manuals; Medical; Medical Records; Mental Health; Methods; Modeling; National Institute of Mental Health; Natural Language Processing; Natural Language Processing pipeline; Performance; Persons; Phenotype; Predictive text; Prevention; Prevention approach; Provider; Public Health; Qualitative Research; Race; Recording of previous events; Research; Research Personnel; Risk; Signal Transduction; Site; Social Psychology; Standardization; Structure; Subgroup; Suicide; Suicide attempt; Suicide prevention; Text; Time; TimeLine; Training; Training Programs; Translating; United States; Universities; Validation; Variant; Visit; Youth; adolescent suicide; aged; base; behavior prediction; blind; career development; case control; comorbidity; cost; demographics; design; evidence base; experience; gradient boosting; health care settings; health inequalities; improved; indexing; machine learning classifier; medical specialties; member; multidisciplinary; natural language; personalized approach; personalized medicine; personalized risk prediction; point of care; prediction algorithm; predictive modeling; prospective; prototype; random forest; responsible research conduct; risk prediction; risk prediction model; sex; simulation; skills; sociodemographics; suicidal behavior; suicidal risk; support vector machine; tool; usability; young adult

PROJECT SUMMARY / ABSTRACT The candidate requests support for a five-year program of training and research to better understand how electronic health record phenotyping and other computational methods applied to existing medical record data can bolster detection and prediction of suicide attempts by children ages 10 to 17. In the proposed training plan, the candidate will build upon her previous experiences in social psychology, clinical informatics, and clinical child and adolescent psychiatry to perform a multidisciplinary project at the University of California, Los Angeles Health System. Her training plan includes developing skills and knowledge in 1) analysis of natural language (text) data, 2) development of risk algorithms in healthcare settings to improve suicide prevention, 3) basic qualitative research skills including modified Delphi Panel approach, and 4) the responsible conduct of research. Suicide is the second leading cause of death of young people over 10 years old in the United States and suicide attempts among children are common, costly and preventable. There is an urgent need to close the gap between risk prediction algorithms and clinically-useable tools that can enhance medical decision- making for providers and families. This study proposes that electronic health record phenotyping, a method of standardizing case detection using clinical note text and structured medical record data, may offer improved detection and personalized risk prediction for children, thus complementing existing suicide prevention efforts. In the proposed research, using a cross-sectional design, Aim 1 will focus on adaptation of electronic health record phenotyping to detect emergency department visits for suicide attempts by children using electronic health records. Then, using a case-control design, Aim 2 will focus on development of risk prediction models of emergency department visits for suicide attempts by children using longitudinal electronic health records over two years. Aim 3 will focus on assessment of the validity, acceptability, usability, feasibility, and overall utility of a personalized risk prediction prototype with case simulations using a modified Delphi panel approach. This plan will parallel a training plan building skills and knowledge to bridge informatics, computational methods, and clinical child psychiatry. In the long term, this research is an initial step to enhance signal detection and support prediction of suicide attempts, in turn, setting the stage for deployment of personalized approaches to prevention in clinical settings where providers, youth, and families may directly benefit.

项目概要/摘要 候选人请求支持为期五年的培训和研究计划，以更好地了解如何 应用于现有医疗记录数据的电子健康记录表型分析和其他计算方法 可以加强对 10 至 17 岁儿童自杀企图的检测和预测。在拟议的培训中 计划中，候选人将基于她之前在社会心理学、临床信息学和 临床儿童和青少年精神病学在加州大学洛杉矶分校开展多学科项目 安吉利斯卫生系统。她的培训计划包括培养以下方面的技能和知识：1) 自然分析 语言（文本）数据，2) 在医疗保健环境中开发风险算法以改善自杀预防，3) 基本的定性研究技能，包括修改后的德尔菲小组方法，以及 4) 负责任的行为 研究。自杀是美国10岁以上年轻人的第二大死因 儿童自杀企图很常见、代价高昂并且是可以预防的。迫切需要关闭 风险预测算法和可以增强医疗决策的临床可用工具之间的差距 为提供者和家庭做事。本研究提出电子健康记录表型分析是一种方法 使用临床记录文本和结构化医疗记录数据标准化病例检测，可能会提供改进的结果 儿童的检测和个性化风险预测，从而补充现有的自杀预防工作。 在拟议的研究中，使用横截面设计，目标 1 将重点关注电子健康的适应 记录表型，以检测使用电子设备的儿童自杀企图的急诊室就诊 健康记录。然后，利用病例对照设计，目标 2 将重点开发以下疾病的风险预测模型： 使用纵向电子健康记录对儿童自杀企图进行急诊科就诊 两年。目标 3 将侧重于评估有效性、可接受性、可用性、可行性和整体效用 使用改进的德尔菲面板方法进行案例模拟的个性化风险预测原型。这 该计划将与培训计划并行，培养技能和知识，以连接信息学、计算方法、 和临床儿童精神病学。从长远来看，这项研究是增强信号检测和识别的第一步。 支持对自杀企图的预测，进而为部署个性化方法奠定基础 临床环境中的预防，提供者、青少年和家庭可能直接受益。