Rethinking Inconsistent Context and Imbalanced Regression in Depression Severity Prediction

被引:0
|
作者
Huang, Guanhe [1 ]
Li, Jing [2 ]
Lu, Heli [3 ]
Guo, Ming [3 ]
Chen, Shengyong [2 ]
机构
[1] Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China
[2] Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300384, Peoples R China
[3] Nanchang Univ, Affiliated Hosp 2, Dept Psychosomat Med, Nanchang 330006, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Depression severity prediction; natural language processing; topic modeling; conditional variational auto-encoder; imbalanced regression; attention mechanism; VARIATIONAL INFERENCE;
D O I
10.1109/TAFFC.2024.3405584
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As one of the world's most prevalent mental illnesses, depression is not easy to detect since it affects different people in different ways. Recently, linguistic features extracted from transcribed texts have been widely explored in depression detection because they contain a variety of cues about psychological activities. However, the detection performance is limited due to the following two reasons: 1) the dialogue structure is ignored, which causes the Inconsistent Context problem; and 2) Imbalanced Regression occurs due to the long-tailed distribution of depression datasets. To this end, in this paper we investigate the relationship between the local topic and global context in interview transcripts, and bridge the gap between depression symptoms and depression severity. In particular, we propose a model called Conditional Variational Topic-enriched Auto-Encoder (CVTAE), which can capture the spatial features from local topics via variational inference, and the temporal features from the global context with attention mechanism. Besides, we apply the re-weighting strategies to assigning weights to the depression labels with different values. Extensive experiments on the DAIC-WOZ dataset in English and a self-constructed database NCUDID in Chinese demonstrate the effectiveness and robustness of CVTAE, while the comprehensive ablation study and case study show its interpretability.
引用
收藏
页码:2154 / 2168
页数:15
相关论文
共 50 条
  • [1] Support Vector Ordinal Regression for Depression Severity Prediction
    Jayawardena, Sadari
    Epps, Julien
    Ambikairajah, Eliathamby
    2018 IEEE 9TH INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION FOR SUSTAINABILITY (ICIAFS' 2018), 2018,
  • [2] Imbalanced regression and extreme value prediction
    Ribeiro, Rita P.
    Moniz, Nuno
    MACHINE LEARNING, 2020, 109 (9-10) : 1803 - 1835
  • [3] Imbalanced regression and extreme value prediction
    Rita P. Ribeiro
    Nuno Moniz
    Machine Learning, 2020, 109 : 1803 - 1835
  • [4] Improving Prediction Accuracy for Logistic Regression On Imbalanced Datasets
    Zhang, Hao
    Li, Zhuolin
    Shahriar, Hossain
    Tao, Lixin
    Bhattacharya, Prabir
    Qian, Ying
    2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1, 2019, : 918 - 919
  • [5] Sparse feature selection and rare value prediction in imbalanced regression
    Guan, Ying
    Fu, Guang-Hui
    INFORMATION SCIENCES, 2024, 680
  • [6] PIXEL PREDICTION BY CONTEXT BASED REGRESSION
    Sheng, Lingyan
    Ortega, Antonio
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 769 - 772
  • [7] Symbolic Regression for Precrash Accident Severity Prediction
    Meier, Andreas
    Gonter, Mark
    Kruse, Rudolf
    HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, HAIS 2014, 2014, 8480 : 133 - 144
  • [8] An intuitionistic fuzzy representation based software bug severity prediction approach for imbalanced severity classes
    Panda, Rama Ranjan
    Nagwani, Naresh Kumar
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 122
  • [9] Traffic accident severity prediction based on oversampling and CNN for imbalanced data
    Shangguan, Anqi
    Mu, Lingxia
    Xie, Guo
    Wang, Chenglan
    Jing, Yang
    Fei, Rong
    Hei, Xinhong
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 7004 - 7008
  • [10] Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms
    Fiorentini, Nicholas
    Losa, Massimo
    INFRASTRUCTURES, 2020, 5 (07)