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

项目摘要/摘要 候选人请求支持一项为期五年的培训和研究计划，以更好地了解 适用于现有病历数据的电子健康记录表型和其他计算方法 可以帮助发现和预测10到17岁儿童的自杀企图。 计划，候选人将以她以前在社会心理学、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进行多学科项目 洛杉矶医疗系统。她的培训计划包括发展自然分析方面的技能和知识。 语言(文本)数据，2)开发医疗保健环境中的风险算法，以改进自杀预防，3) 基本的定性研究技能，包括改进的Delphi Panel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 而儿童自杀企图是常见的，代价高昂，而且是可以预防的。有迫切的需要关闭 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- 为供养者和家庭服务。这项研究提出，电子健康记录表型，一种方法 使用临床病历文本和结构化病历数据标准化病例检测可能会提供改进 对儿童进行检测和个性化风险预测，从而补充现有的自杀预防努力。 在拟议的研究中，采用横断面设计，目标1将重点放在电子健康的适应上 使用电子设备记录表型以检测儿童自杀企图的急诊科就诊 健康记录。然后，使用病例对照设计，目标2将专注于开发风险预测模型 使用纵向电子健康记录的儿童自杀企图的急诊科就诊超过 两年了。目标3将侧重于评估以下各项的有效性、可接受性、可用性、可行性和整体效用 一个个性化的风险预测原型，使用改进的Delphi面板方法进行案例模拟。这 计划将与培训计划并行，建立技能和知识之间的桥梁信息学，计算方法， 和临床儿童精神病学。从长远来看，这项研究是加强信号检测和 支持自杀未遂的预测，进而为部署个性化方法铺平道路 在提供者、青年和家庭可能直接受益的临床环境中进行预防。

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.