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.

项目摘要/摘要