文献类型：期刊文献

英文题名：Depressive tendency detection based on contextual modeling and its exploratory use in intervention

作者：Deng, Shasha[1,2];Wang, Qiaoxia[2];Yan, Wenqi[2];Liu, Xuan[3]

机构：[1]Shanghai Int Studies Univ, Shanghai Key Lab Brain Machine Intelligence Inform, Shanghai, Peoples R China;[2]Shanghai Int Studies Univ, Sch Business & Management, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China

年份：2025

起止页码：1

外文期刊名：INDUSTRIAL MANAGEMENT & DATA SYSTEMS

收录：;EI(收录号：20255019710583);WOS:【SCI-EXPANDED(收录号:WOS:001618661000001)】；

基金：This work was supported by the National Natural Science Foundation of China (Award numbers: 72071131, 72471150 and 71971082).

语种：英文

外文关键词：Depressive tendency detection; Contextual modeling; Multidimensional feature fusion; Attention mechanism

摘要：PurposeDepressive tendency, a precursor to major depressive disorder (MDD), is frequently undetected by existing early-warning systems. Traditional methods, based on univariate metrics or subjective inventories, neglect the multidimensional interactions of biomarkers essential for accurate diagnosis. This study aims to enhance the accuracy of depressive tendency detection, thereby contributing to the reduction of MDD incidence and alleviation of its societal burden through early warning and exploratory intervention use.Design/methodology/approachThis study proposed a multidimensional model for detecting depressive tendency, integrating emotional, behavioral, cognitive, physiological and temporal features into a contextual modeling framework. The model combines bidirectional long short-term memory with an attention mechanism to improve detection accuracy and robustness. It has also been integrated into a virtual psychological intervention platform to support user interaction and exploratory intervention use.FindingsThe proposed model effectively captures complex manifestations of depressive tendency, including emotional fluctuations, behavioral dynamics and physiological patterns, particularly in social media data. It outperforms existing models in detection accuracy. An exploratory study within a virtual psychological intervention platform demonstrates the model's practical utility for early detection and intervention.Originality/valueThis study integrates multidimensional psychological and physiological signals into a contextual modeling framework, overcoming limitations of traditional MDD detection methods. The proposed model enhances early detection accuracy and generalizes well across social media datasets. Its practical utility in virtual intervention use highlights its potential for proactive and personalized mental health support.