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: 10705670
• 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/10705670
• 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 Value; 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; Training; Training Programs; Translating; United States; Universities; Validation; Variant; Visit; Youth; adolescent suicide; aged; blind; career development; case control; comorbidity; cost; demographics; design; detection method; 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; predictive modeling; predictive tools; prospective; prototype; random forest; responsible research conduct; risk prediction; risk prediction model; sex; simulation; skills; sociodemographics; suicidal behavior; suicidal risk; support vector machine; timeline; 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.

项目总结/摘要 候选人要求支持一个为期五年的培训和研究计划，以更好地了解如何 电子健康记录表型分析和应用于现有医疗记录数据的其它计算方法 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，候选人将建立在她在社会心理学，临床信息学， 临床儿童和青少年精神病学在洛杉矶加州大学进行一个多学科项目 洛杉矶卫生系统她的培训计划包括发展分析自然环境的技能和知识， 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， research.自杀是美国10岁以上青少年死亡的第二大原因 儿童自杀未遂是常见的，代价高昂，但可以预防。迫切需要关闭 风险预测算法和临床可用工具之间的差距，可以增强医疗决策- 为供应商和家庭。这项研究提出，电子健康记录表型，一种方法， 使用临床笔记文本和结构化医疗记录数据来标准化病例检测可以提供改进的 为儿童提供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将侧重于电子健康的适应 记录表型，以检测急诊室就诊的自杀企图的儿童使用电子 健康记录。然后，使用病例对照设计，目标2将侧重于开发风险预测模型， 使用纵向电子健康记录的儿童自杀未遂的急诊室就诊， 两年目标3将侧重于评估的有效性，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与案例模拟使用修改后的德尔菲面板方法。这 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，这项研究是加强信号检测和 支持自杀企图的预测，反过来，为部署个性化的方法， 在临床环境中预防，提供者，青年和家庭可能直接受益。