在临床记录中发现青少年自杀干预的检测 文本使用开源语言模型
Juliet B Edgcomb1, Alexandra Klomhaus1, Joshua Lee1
1University of California, Los Angeles, Los Angeles, California, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
一个人工智能模型准确地识别了青年自杀干预的紧急笔记. 这种自动化方法有助于改善护理,突出了为处于危险的年轻人提供文档干预措施的差距.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 医疗保健中的人工智能
背景情况:
- 青少年自杀是一个关键的公共卫生问题.
- 紧急部门是识别和干预青少年自杀倾向的关键点.
- 在临床笔记中记录自杀预防干预措施对于护理质量至关重要.
研究的目的:
- 开发和验证一种自动化方法,用于检测青年自杀预防干预措施在急诊部 (ED) 笔记.
- 评估开源语言模型在识别特定干预措施方面的性能.
- 为了确定与缺失的干预相关的因素在ED遇到青年自杀倾向.
主要方法:
- 利用专家评价,将四个关键干预措施 (致命手段限制,热线,门诊转诊,安全规划) 分类为1,794个ED笔记.
- 使用开源语言模型Llama3.3-70B生成干预存在的Likert分数,为每个干预实现高AUROC值.
- 分析了6687条来自723次接触的笔记,以确定干预疏忽的预测因素.
主要成果:
- 语言模型在检测干预方面表现出高精度,AUROC值在0.935到0.980.之间.
- 青少年有之前的ED访问,缺少查问题,或提出思想 (与行为/企图相比) 增加了遗漏干预的几率.
- 研究结果表明,系统的临床文本分析可以揭示改善自杀预防护理的目标.
结论:
- 使用开源语言模型对临床文本进行自动化分析是可行且准确的,用于识别青少年自杀预防干预措施.
- 该研究确定了特定的患者特征和与已记录的干预措施中的差距相关的遭遇类型.
- 这种方法可以为决策支持工具提供信息,并增强基于证据的青少年自杀预防策略.
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