探索第一人称单数代词在检测自杀念头中的作用:对临床成绩单的机器学习分析
Rong Huang1,2, Siqi Yi1,2, Jie Chen1,3
1Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.
Behavioral sciences (Basel, Switzerland)
|March 28, 2024
概括
临床访谈中的第一人称单数代词 (FPSP) 可能表明中国患者的自杀念头. 机器学习模型显示了帮助临床医生检测自杀风险的潜力.
科学领域:
- 计算语言学 计算语言学
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 语言特征,特别是第一人称单数代词 (FPSP),是潜在的自杀想法指标.
- 机器学习 (ML) 和自然语言处理 (NLP) 在自杀检测方面表现有前途,但临床应用需要进一步探索.
研究的目的:
- 识别与自杀念头相关的语言特征.
- 开发和评估使用临床访谈的语言特征来检测自杀念头的ML模型.
主要方法:
- 使用自然语言处理 (NLP) 技术,从319份临床访谈成绩单中提取语言特征,包括四个第一人称单数代名词 (FPSPs) 和第一人称复数代名词 (FPPPs) 的案例.
- 进行了后勤回归分析,以评估语言特征与自杀念头的关联,控制年龄,性别和抑郁症.
- 几个ML算法,包括梯度增强,支持向量机,随机森林,决策树和后勤回归,都被训练并评估了它们的检测性能.
主要成果:
- 所有四个FPSP病例都与抑郁症有显著的关联 (p < 0.05).
- 使用客观的FPSP是唯一与自杀念头有显著关联的语言特征 (p = 0.02).
- 后勤回归和支持矢量机器模型成功检测了自杀念头,实现了0.57的曲线下面积 (AUC) (p < 0.05).
结论:
- 从临床采访中提取的第一人称单数代词 (FPSPs) 可能成为中国患者自杀念头的有前途指标.
- 机器学习算法显示出潜在的工具,以帮助临床医生加强在临床环境中检测自杀念头.
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