通过零射击学习在临床笔记中识别自杀记录
Terri Elizabeth Workman1,2, Joseph L Goulet3,4, Cynthia A Brandt3,4
1Biomedical Informatics Center The George Washington University Washington District of Columbia USA.
Health science reports
|September 14, 2023
概括
这项研究引入了零射击学习,以识别临床笔记中的自杀倾向,克服数据限制. 这种新的方法能够准确地检测到没有手册注释的自杀风险文档.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 在临床笔记中识别自杀倾向是具有挑战性的,因为培训数据有限.
- 积极实例的稀缺性阻碍了用于自杀风险检测的深度学习模型的开发.
研究的目的:
- 开发一种新的方法,使用零射击学习来识别临床笔记中的自杀倾向.
- 为了解决临床笔记分析的深度学习中的数据稀疏性问题.
主要方法:
- 使用了美国退伍军人事务所的临床笔记,标签来源于自杀企图和自伤诊断代码.
- 采用深度神经网络,以辅助信息的基础字符串进行训练,将文档内容映射到语义空间.
- 与基线深度神经网络的性能进行了比较,使用了词包功能.
主要成果:
- 零射击学习模型在各种指标上显著超过了基线,包括曲线下的面积,灵敏度和特异性.
- 在识别与ICD-10-CM代码无关的自杀性文档时,在0.90概率值下获得了94%的准确性.
结论:
- 零射击学习提供了一种有效的方法来识别临床笔记中的自杀倾向.
- 这种方法可以在不需要手动注释的情况下准确检测自杀风险,解决数据限制.
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