从p因素到认知内容:基于可解释的人工智能的精神病理的检测和歧视
1Department of Clinical Psychology, Graduate School of Social Science, Ankara University, Ankara, Türkiye.
Depression and anxiety
|May 27, 2025
概括
先进的自然语言处理和机器学习可以识别语言标记,以区分抑郁症和焦虑症. 这些计算技术显示出开发新的基于语言的心理健康诊断工具的前景.
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.
- 机器学习在医疗保健中的应用.
背景情况:
- 由于像p因子这样的共享机制,区分精神病理很困难.
- 独特的语言标记可能有助于检测和区分心理健康状况.
- 自然语言处理 (NLP) 和机器学习 (ML) 提供了新的分析可能性.
研究的目的:
- 使用认知内容分析区分控制,抑郁,焦虑和抑郁焦虑组.
- 用NLP和ML技术检测一般的精神病理.
- 探索先进的计算方法在识别心理健康状况的语言标志物的实用性.
主要方法:
- 从1901名参与者的贝尔句子完成测试 (BSCT) 数据分析.
- 使用抑郁,焦虑和压力表 (DASS-21) 和积极和消极影响表 (PANAS) 的小组组建.
- 应用来自变压器的双向编码器表示 (BERT) 来进行文本分析和支持矢量机 (SVM) 来进行子尺度得分分析,并使用夏普利添加式解释 (SHAP) 来进行解释.
主要成果:
- 模型成功地区分了控制,抑郁,焦虑和抑郁焦虑组.
- 焦虑与积极的内容和希望相关;抑郁与负面的内容和绝望相关.
- 模型在区分当前的病理诊断方面表现出很高的表现 (AUC:0.81-0.85),但在过去的诊断方面遇到了困难 (AUC:0.53-0.57),这表明心理病理学的共同底层结构.
结论:
- 先进的计算技术可以识别心理健康状况的关键语言标记.
- 这些发现支持开发基于语言的新型诊断工具.
- NLP和ML的整合有可能改变心理健康诊断.
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