在临床上患精神病高风险的年轻人中进行自动化语言分析
Elif Kizilay1, Berat Arslan1, Burcu Verim1
1Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey.
Schizophrenia research
|September 18, 2024
概括
语音的自然语言处理可以识别患有精神病临床高风险 (CHRP) 的个人. 语言分析揭示了CHR-P个体的独特语音模式,有助于早期精神病干预.
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
- 机器学习 机器学习
背景情况:
- 早期识别患有精神病临床高风险 (CHRP) 个体对于干预和改善精神分裂症结果至关重要.
- 在CHR-P个体中,轻微的形式思维障碍 (FTD) 提供了使用自然语言处理 (NLP) 检测的机会.
研究的目的:
- 研究NLP方法在识别CHR-P个体FTD语言标记物的实用性.
- 开发和评估一种机器学习模型,以基于语音特征来区分CHR-P个体与健康对照 (HCs).
主要方法:
- 采集了62名CHR-P个人和45名HC的语音样本,使用主题感知测试图像.
- 语言分析包括语义相似性,通用特征 (例如,词量,句子长度) 和部分语音 (POS) 标记.
- 一个机器学习模型使用30个NLP衍生功能的训练.
主要成果:
- CHR-P组显示出更高的句子级语义相似性和更低的图像与文本相似性.
- 在CHR-P组中观察到,言语量减少,句子缩短,单词缩短.
- POS分析表明,副词,连接词和第一人称单数代词的使用减少,CHR-P个体的形容词使用增加.
- 机器学习模型的准确度达到79.6%,AUC-ROC为0.86.
结论:
- 使用NLP的自动语音分析可以有效地鉴定患有精神病临床高风险的个体的FTD.
- 这些发现支持客观语言分析在早期精神病检测和干预方面的潜力.
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