在第一期双相情感障碍和精神病的语音样本中对语言特征的计算分析
Berat Arslan1, Elif Kizilay1, Burcu Verim1
1Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey.
Journal of affective disorders
|July 19, 2024
概括
自然语言处理 (NLP) 方法在第一发精神病 (FEP) 和第一发双相情感障碍 (FEBD) 中发现了明显的语言异常. 与FEBD相比,FEP表现出更广泛的语言差异,这表明了跨诊断标记.
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
- 神经科学是一个神经科学.
背景情况:
- 使用NLP的自动语言分析越来越多地用于精神分裂症.
- 很少有研究将这些方法应用于双相情感障碍.
- 没有先前的研究比较了第一发精神病 (FEP) 和第一发双相情感障碍 (FEBD) 的自动化语言特征.
研究的目的:
- 使用NLP方法比较FEP和FEBD的自动化语言特征.
- 在FEP和FEBD中识别跨诊断性语言异常.
主要方法:
- 包括53个FEP,40个FEBD和50个健康对照 (土耳其语为母语).
- 来自主题感知测试 (TAT) 的语音样本经历了自动通用,部分语音和句子级语义相似性分析 (SBERT).
主要成果:
- 无论是FEBD还是FEP,都显示短句和语义相似性增加,与TAT图片的对齐性较小.
- 与FEBD相比,FEP呈现出较少的语言和语法复杂性.
- 分类准确率为82.45% (FEP与HC),71.1% (FEBD与HC) 和73% (FEP与FEBD). 在FEP中,负面症状与言语输出减少和语义相似性增加相关.
结论:
- 无论是FEP和FEBD组都表现出语言异常.
- 语言异常在FEP中比在FEBD中更严重和更广泛.
- NLP方法有效地揭示了FEP和FEBD中的跨诊断性语言异常.
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