验证自然语言处理方法,捕捉非情感精神病患者言语中的语义不一致
Sandra Anna Just1, Anna-Lena Bröcker1, Galina Ryazanskaya2
1Department of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
Frontiers in psychiatry
|August 11, 2023
概括
自然语言处理 (NLP) 显示出在非情感精神病 (NAP) 中客观分析语音的前景. Word2vec模型与临床结果的相关性比GloVe更好,尽管纵向数据需要进一步研究.
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
- 语音分析 语言分析
背景情况:
- 语音产生障碍是非情感性精神病 (NAP) 的一个关键症状.
- 传统的临床语音评分是主观的.
- 自然语言处理 (NLP) 为NAP患者提供客观的语音分析.
研究的目的:
- 验证NLP方法用于分析非情感精神病中的语音产生.
- 为了比较不同的NLP模型和词嵌入的有效性.
主要方法:
- 从精神分裂症或精神分裂情绪障碍患者的语音样本在两个时间点收集.
- 应用了全球和本地语义连贯性模型和词嵌入 (word2vec, GloVe).
- 测试模型与临床评级和外部标准的相关性.
主要成果:
- 全球和地方一致性模型之间没有发现显著差异.
- Word2vec模型与GloVe模型相比,与临床结果的相关性更为显著.
- 对纵向数据的探索性分析没有与一致性得分产生显著的相关性.
结论:
- 在NPT中,NLP方法需要进行批判性验证和仔细选择,以便在NPT中临床应用.
- 在横截面分析方面,Word2vec似乎比GloVe更有前途.
- 需要进一步的研究,特别是在精神病中进行纵向语音分析.
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