不确定性意识域增量学习用于跨域抑郁检测

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Updated: Sep 15, 2025

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Zita Lifelo¹, Jianguo Ding², Huansheng Ning¹

¹School of Computer and Communications Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

Scientific reports

|July 14, 2025

概括

这项研究引入了一种新的框架,用于使用文本数据检测严重抑郁症 (MDD),解决跨领域设置中的隐私和数据限制. 该方法通过管理域间隙和模型不确定性来提高抑郁症检测的准确性.

科学领域:

计算精神病学是一种计算精神病学.
机器学习在心理健康中的应用.

背景情况:

深度学习显示出从文本中检测主要抑郁症 (MDD) 的前景.
现有的方法在有限的数据,隐私,域间隙和现实应用中的不确定性方面扎.

研究的目的:

为跨领域抑郁症检测 (UDIL-DD) 提出一个不确定性意识领域增量学习框架.
为了克服数据隐私,领域差距,阶级不平衡和抑郁症检测不确定性的挑战.

主要方法:

开发了不确定性引导的自适应类值学习 (UACTL),以测量预测差异并纳入不确定性.
实现了数据免费域调整 (DFDA),以在不访问先前数据的情况下近似历史特征分布,减轻灾难性遗忘.

主要成果:

UDIL-DD框架在跨领域抑郁症检测方面表现出有效性.
在四个基准数据集 (CMDC,DIAC-WoZ,MODMA,EATD) 上的实验验证实了该方法的稳定性.

结论:

拟议的UDIL-DD框架提供了一种可靠的方法,用于在现实世界的临床场景中检测抑郁症.
整合UACTL和DFDA有效地处理数据隐私和域名转移的挑战.

关键词:

阶级不平衡造成的不平衡没有数据的域调整对齐.增量学习是一种增量学习.大型抑郁症主要是抑郁症.不确定性估计估计不确定性

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