心理健康干预的自然语言处理:系统审查和研究框架
Matteo Malgaroli1, Thomas D Hull2, James M Zech2,3
1Department of Psychiatry, New York University, Grossman School of Medicine, New York, NY, 10016, USA. matteo.malgaroli@nyulangone.org.
Translational psychiatry
|October 5, 2023
概括
自然语言处理 (NLP) 显示出通过检查对话数据来分析心理健康干预措施 (MHI) 的前景. 本系统性审查突出了NLP-MHI研究的快速增长,确定了关键趋势,并提出了改善其临床应用和公平性的框架.
科学领域:
- 计算语言学和数字心理健康.
- 人工智能在医疗保健中的应用.
背景情况:
- 神经精神疾病带来了巨大的社会成本,治疗的有效性往往受到客观结果和忠实度指标的缺乏限制.
- 自然语言处理 (NLP) 通过处理对话数据为分析心理健康干预 (MHI) 提供了一种新的方法,但其充分的临床和研究潜力仍未得到充分探索.
研究的目的:
- 系统地审查自然语言处理在心理健康干预中的应用.
- 评估NLP模型,临床应用,并确定现有研究中的偏见和差距.
- 开发一个框架 (NLPxMHI) 来推进MHI中的NLP.
主要方法:
- 一个预先注册的系统审查,遵守PRISMA指南.
- 包括来自各种数据库 (PubMed,PsycINFO,Scopus,Google Scholar,ArXiv) 的102篇文章,截至2023年1月.
- 分析计算和临床特征,包括NLP算法,特征,基本真理和临床类别.
主要成果:
- 自2019年以来,NLP-MHI研究的显著增加,随着样本规模的增加和大型语言模型的使用.
- 数字健康平台是主要的数据来源;基于文本的功能在模型准确性方面通常优于音频标记器.
- 常见的临床重点包括患者表现,干预反应和提供者特征.
结论:
- 尽管取得了进展,但在NLP-MHI研究中,语言多样性缺乏,可复制性受限和人口偏见等局限性仍然存在.
- 建议制定一个验证的研究框架 (NLPxMHI),以指导计算和临床研究人员解决这些差距.
- 该框架旨在提高NLP在心理健康干预中的应用的临床实用性,数据可访问性和公平性.
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