通过使用分类和可解释性模型,更深入地了解超高风险患者的语言特征
Deok-Hee Kim-Dufor1, Michel Walter2, Marie-Odile Krebs3
1Limics, Sorbonne Université, Université Sorbonne Paris-Nord, INSERM, Paris, France.
Frontiers in psychiatry
|July 1, 2025
概括
这项研究确定了关键的语言标志物,如弱连贯性,以对患有精神病超高风险的患者进行分类. 了解这些语言模式有助于早期发现和分析精神病风险.
科学领域:
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 语言的特殊性是已知的精神分裂症的指标.
- 自然语言处理 (NLP) 和机器学习 (ML) 在区分精神病风险组方面表现有前途.
- 具体的语言标记及其对分类的贡献需要进一步阐明.
研究的目的:
- 识别语言标记,用于对患有精神病超高风险的患者进行分类.
- 解释这些标记物如何为患者分类做出贡献.
- 为了增强对精神病中语言表现的理解.
主要方法:
- 记录和转录了68名患者的精神病学咨询 (分类为非危险,危险和第一发精神病).
- 利用NLP技术来分析词汇丰富性,语义连贯性,语音不流利性和语法复杂性.
- 采用渐变增强的决策树算法与合成少数人过量采样技术 (SMOTE) 和夏普利添加式扩展 (SHAP) 进行分类和特征重要性分析.
主要成果:
- 该分类模型实现了高性能指标:0.82准确度,0.82F2得分,0.85精度,0.82回忆和0.86ROC-AUC.
- 发现的关键语言变量是连贯性较弱,使用"I",填写暂停.
- SHAP值显示了这些特征对患者分类的差异性贡献.
结论:
- 虚弱的语义连贯性成为将患有精神病超高风险的患者分类的关键因素.
- 虽然统计测试没有显示出"I"的使用或填充暂停的显著差异,但可解释性模型突出了它们在分类中的独特作用.
- 对语言特征的详细分析提供了对患者群体之间微妙的语言差异的更深入的见解,有助于更好地理解和分析语言行为.
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