从自由文本临床记录中开发一种用于识别临床风险的工具:自然语言处理研究
Natasha Biscoe1, Daniel Leightley2, Dominic Murphy1,2
1Combat Stress Centre for Applied Military Health Research, Leatherhead, United Kingdom.
JMIR AI
|September 22, 2025
概括
这项研究开发了一种自然语言处理工具,用于识别使用电子健康记录的退伍军人中高和低的自杀风险. 该工具准确地对风险进行了分类,证明了其在心理健康支持方面的潜力.
科学领域:
- 计算语言学计算语言学
- 临床信息学是一种临床信息学.
- 心理健康研究 心理健康研究
背景情况:
- 电子病例记录 (EPR) 是研究中未充分利用的丰富数据来源.
- 自然语言处理 (NLP) 已经应用于EPR,但在第三部门组织中并不广泛.
- 退伍军人精神健康慈善机构拥有宝贵的EPR数据用于风险评估.
研究的目的:
- 开发基于NLP的风险识别工具,以辨别高和低的自杀风险.
- 为了开发工具,利用来自英国的退伍军人心理健康慈善机构的EPR.
- 将机器学习分类应用于老兵的临床笔记.
主要方法:
- 从经验丰富的EPR中提取了20342份临床笔记.
- 在训练和测试数据集中使用了70/30的分割.
- 开发和训练了一种二进制分类框架 (1=高风险,0=低风险).
主要成果:
- 一个物流回归分类器证明了最佳性能.
- 该工具实现了0.74的平均正预测值和0.73.7的负预测值.
- 评估的性能指标包括灵敏度 (0.75),F1得分 (0.74) 和通过尤登指数 (0.73) 的准确性.
结论:
- 从临床笔记中,NLP工具成功地确定了退伍军人的自杀风险类别.
- 该工具对加强退伍军人的心理健康护理有很大的希望.
- 未来的研究应该探索该工具能够检测微妙的风险差异及其通用性的能力.
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