检测儿童急救服务的自杀和自我伤害:对3828次遭遇的多模式分析
Juliet Beni Edgcomb1, Angshuman Saha2, Alexandra Klomhaus2
1Mental Health Informatics and Data Science (MINDS) Hub, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 760 Westwood Plz, Los Angeles, CA, 90095, United States, 1 310-825-9989.
JMIR mental health
|February 4, 2026
概括
通过将电子健康记录结构化数据与临床笔记文本相结合,在急诊中精确检测儿科自我伤害思想和行为 (SITB) 得到了改进. 混合方法减少了不同年轻人群的检测变异性.
科学领域:
- 儿科急诊医学 儿科急诊医学
- 医疗信息学 医疗信息学
- 心理健康研究 心理健康研究
背景情况:
- 自杀是美国儿童死亡的主要原因,但准确测量紧急服务对自我伤害思想和行为 (SITB) 的使用是具有挑战性的.
- 目前使用诊断码的诊断方法低估了儿科病例,阻碍了公共卫生和预防工作.
- 现有研究尚未确定最佳的电子健康记录 (EHR) 数据元素组合,以准确和一致地检测青少年的SITB.
研究的目的:
- 为了比较基于EHR的方法用于识别SITB相关的儿科急诊室 (ED) 访问的准确性.
- 评估每个方法的不同青年人口统计和心理健康诊断的检测变异性.
- 评估基本结构化数据,全面结构化数据,使用自然语言处理 (NLP) 的临床笔记文本和混合方法.
主要方法:
- 对3828次儿科精神卫生ED访问和28861份临床笔记的回顾性审查.
- 人类专家使用哥伦比亚自杀评估分类算法来标记SITB存在.
- 使用结构化数据 (不同维度),文本数据 (NLP,LLM衍生分数) 和混合方法 (结构化数据+文本) 开发了随机森林分类器.
主要成果:
- 随着数据维度的提高,检测准确性得到了提高,高维度方法实现了0.965.96的AUROC.
- 低维结构化数据 (ICD代码,主要关注点) 显示了高检测可变性,在青春期前,男性和特定疾病群体的准确性较低.
- 结合增强病例监测 (aCS) 与大型语言模型元AI (LLM-MAI) 的混合方法实现了最佳性能 (AUROC=0.977),在大多数子组中具有高精度.
结论:
- 混合结构文本EHR数据检测方法显著提高准确性,并减少与单独诊断代码相比的变化.
- 这些发现为部署改善的信息检索系统为儿科自杀和自我伤害紧急情况提供了基础.
- 强化检测方法对于有效的公共卫生监测和干预策略至关重要.
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