从高风险社会挑战记录中使用语言和声学测量预测超高风险结果:mHealth纵向队列探索性研究
Samuel Ming Xuan Tan1, May Yen Lieu1, Jun Kai2
1LKC School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Experimental Medicine Building, Singapore, 636921, Singapore, 65 65927871.
JMIR formative research
|December 30, 2025
概括
使用高风险社会挑战 (HiSoC) 任务的超高风险 (UHR) 个体的语音分析揭示了语言标志物,如语音频率下降和流利性增加,预测精神病转化. 这种可扩展的方法有助于早期发现精神病.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算语言学 计算语言学
- 医疗保健中的机器学习
背景情况:
- 早期发现精神病超高风险 (UHR) 对干预至关重要,但目前的方法缺乏特异性.
- 基于语音的机器学习有可能提高UHR个体的预后准确性.
- 现有的语音任务通常很长,限制了可扩展性;高风险社会挑战 (HiSoC) 是一个简短的45秒任务,评估社会功能.
研究的目的:
- 调查HiSoC任务中的语言和声学特征是否与UHR结果相关.
- 确定这些语音特征对不同UHR轨迹 (转化,缓解,维护) 的预测能力.
主要方法:
- 收集了41名UHR参与者的HiSoC任务的音频录音.
- 分析语言特征 (例如,每分钟的单词,不流) 和声学特征 (例如,基本频率,强度).
- 采用多变量线性回归和线性支向量机器,用于分析和预测的嵌套交叉验证,通过平衡精度进行评估.
主要成果:
- 转化为精神病的小组与转化为精神病的小组相比,每分钟单词显著减少,流利性更高.
- 在结局组之间没有观察到关节率,序列连贯性或声学测量方面的显著差异.
- 使用语言特征 (BA 0.741) 和综合语言声学特征 (BA 0.851) 的预测模型优于随机机会.
结论:
- 简短的HiSoC语音任务中的语言特征显示了UHR结果组之间的显著差异.
- 研究结果支持使用HiSoC语音记录来预测UHR个体的缓解和转化.
- HiSoC任务提供了一种可扩展的方法,用于识别UHR人群中的预测性语音信号.
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