对比传统的自然语言处理和大语言模型用于心理健康状态分类:一个多模型评估
Thomas Kallstenius1, Andrea Johansson Capusan2, Gerhard Andersson3,4
1Trädtopp, Tervuren, Belgium.
Scientific reports
|July 6, 2025
概括
传统的自然语言处理 (NLP) 与先进的特征工程技术在心理健康分类中实现了95%的准确性,超过了大型语言模型 (LLM). 专门的NLP模型对于关键的医疗保健应用是优越的.
科学领域:
- 计算精神病学是一种计算精神病学.
- 数字心理健康数字心理健康
- 自然语言处理自然语言处理.
背景情况:
- 全球精神健康障碍的增加需要可扩展的检测工具.
- 数字环境为自动心理健康分类提供了平台.
- 现有的计算方法需要严格评估准确性和可靠性.
研究的目的:
- 为了比较传统NLP,即时设计的LLM和微调的LLM用于自动心理健康分类的有效性.
- 为了评估分类准确性,精度,回忆和F1分数在七种心理健康状况中的准确性.
- 确定在数字文本中可靠地检测心理健康的最有效的计算方法.
主要方法:
- 利用了超过51,000个社交媒体文本陈述的数据集,标记了七种心理健康状况.
- 实施并比较了三种方法:传统的NLP与特征工程,提示工程的LLM和微调的LLM.
- 在微调的LLM中使用跨训练时代的验证损失监控过.
主要成果:
- 传统的NLP与先进的特征工程实现了95%的准确性,显著优于提示性工程LLM (65%) 和微调LLM (91%).
- 专门的NLP模型表现出卓越的准确性和精度.
- 三个时代的微调LLM产生了最佳的结果,进一步的培训导致了过度装配.
结论:
- 在传统的NLP模型中,先进的文本预处理和特征工程为心理健康分类提供了显著的好处.
- 使用快速工程的现成LLM对于准确的心理健康分类是不够的.
- 专门的计算方法,特别是增强的传统NLP,对于关键医疗保健应用,如心理健康分类,仍然优于通用LLM.
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