在土耳其语患者的语音样本中进行自动化语言分析,这些患者患有精神分裂症谱系障碍
Berat Arslan1, Elif Kizilay1, Burcu Verim1
1Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey.
Schizophrenia research
|March 22, 2024
概括
自然语言处理 (NLP) 在土耳其人中准确地识别了精神分裂症谱系障碍 (SSD). 使用语义和语言特征的机器学习模型实现了高分类准确性,有助于客观语言评估.
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.
- 人工智能的人工智能
背景情况:
- 自然语言处理 (NLP) 为量化语言障碍提供了客观方法.
- 这些方法有可能用于精神疾病的诊断分类.
- 以前的NLP研究主要集中在英语人口中.
研究的目的:
- 为了评估NLP技术在分类精神分裂症谱系障碍 (SSD) 的有效性,在土耳其语人士.
- 探索特定的语言特征,包括语义相似性和部分语音 (POS) 模式,用于诊断潜力.
- 评估定量语言特征与临床措施的相关性,例如负面症状严重程度和疾病持续时间.
主要方法:
- 来自82名土耳其语受试者的语音样本的计算机语言分析 (44名SSD,38名健康对照).
- 使用的句子双向编码器从文本 (SBERT) 来表示16个句子级语义相似性特征.
- 通过使用随机森林分类器提取了8个通用和8个POS特征.
主要成果:
- 随机森林分类器使用SBERT衍生的语义特征实现了85.6%的准确性,用于SSD与健康对照分类.
- 将语义,通用和POS功能结合起来,提高了准确度至86.8%.
- 与对照组相比,SSD患者表现出增加的句子级语义相似性,更多地使用动词,自称名词和代词,减少使用连接词,决定词和短句长度.
结论:
- NLP方法,特别是那些利用语义相似性的方法,在土耳其语说者中对SSD进行分类时表现出高准确度.
- 通过NLP识别的语言特征与临床评估的负面症状和疾病持续时间相关.
- 这些发现支持扩大基于NLP的诊断工具,用于非英语口语的精神病患者群体.
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