精神病症状纵向变化的自动语音和语言标记
Sunny X Tang1,2,3,4, Michael J Spilka5,6, Majnu John1,2
1Northwell Health, New Hyde Park, NY USA.
NPP - digital psychiatry and neuroscience
|June 20, 2025
概括
自动语音和语言分析可以随着时间的推移跟踪精神病症状. 这些客观标志物显示出对心理障碍患者疾病严重程度的监测有前途.
科学领域:
- 心理语言学 心理语言学
- 计算精神病学是一种计算精神病学.
- 临床神经科学 临床神经科学
背景情况:
- 精神病的特点是复杂的症状领域,包括思维障碍,负面症状和积极症状.
- 对这些领域的纵向跟踪对于了解疾病进展和治疗疗效至关重要.
- 需要客观,可扩展的生物标志物来补充传统的临床评估.
研究的目的:
- 评估自动语音和语言特征,用于纵向跟踪精神病领域.
- 识别与思维障碍,负面症状和积极症状相关的特定语音和语言标记.
- 评估这些关系的稳定性和时间动态.
主要方法:
- 66名患有精神疾病的参与者在6个月的时间内进行了纵向评估.
- 半结构面试和标准化尺度测量了精神病症状.
- 自动分析357个来自各种任务 (阅读,图片描述等) 的语音和语言特征. ) 的情况.
- 线性混合模型分析了特征和症状领域之间的关系.
主要成果:
- 三个精神病领域 (思维障碍,消极,积极) 都显示出与复合语音/语言得分有显著的纵向关系.
- 思想障碍与句子的复杂性和语义特征相关.
- 负面症状与语音复杂性下降有关;正面症状与特定特征 (如名词使用和语义距离) 有微妙的关系.
结论:
- 自动语音和语言功能为客观,可扩展的精神病严重程度监测提供了一个有希望的途径.
- 特定的语言模式与不同的精神病领域相关,有助于细微的评估.
- 需要进一步的研究来优化临床翻译,考虑到患者群体和环境.
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