一种评估慢性失眠的新方法:基于机器学习的TCM观察数字特征的融合
Yu Wang1,2, Jie Chen3,4, Qincheng Chen5
1School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Digital health
|November 28, 2025
概括
对慢性失眠的客观评估是具有挑战性的. 这项研究开发了一种机器学习模型,使用面部和舌头特征来准确评估失眠严重程度,提供一种非侵入性诊断方法.
科学领域:
- 综合医学是一个整体的医学.
- 医疗信息学医学信息学
- 传统中国医药 传统中国医药
背景情况:
- 慢性失眠的临床评估面临的挑战是昂贵和复杂的客观方法,如多睡眠学.
- 需要客观,多样化和易于使用的方法来评估慢性失眠.
研究的目的:
- 开发和验证一种用于评估慢性失眠严重性的非侵入性分类模型.
- 通过机器学习探索面部和舌头特征在慢性失眠诊断中的实用性.
主要方法:
- 收集了594名慢性失眠患者的临床数据,包括通过舌头面部诊断分析-1仪器分析的面部和舌头特征.
- 在变量选中采用渐进回归,主要成分分析和零膨胀负二项式 (ZINB) 回归.
- 使用六种监督机器学习方法构建了一个分类模型,对灵敏度,特异性,F1得分,精度和准确性进行评估,并使用SHAP和DCA进行可视化.
主要成果:
- 模型4,整合基线数据,睡眠症状和面部特征,表现出卓越的性能,接收器运行特征曲线值为0.822.
- 决策曲线分析 (DCA) 证实了模型4的显著临床实用性.
- 沙普利添加式扩展 (SHAP) 突出了特定的传统中医特征 (Ch-Y,CH-G,Ch-R) 在预测失眠严重程度的突出地位,与传统尺度 (PSQI,SAS) 相比.
结论:
- 这项研究成功开发了一种方便且非侵入性的分类模型,用于评估慢性失眠的严重程度.
- 面部和舌头特征的整合为诊断和治疗慢性失眠提供了有前途的进展.
- 这些发现支持在现代机器学习模型中使用基于传统中医学的特征,以提高临床效用.
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