Academic Journal
Multimodal fusion of speech and PHQ-9 for machine learning-based adolescent depression screening.
| Τίτλος: | Multimodal fusion of speech and PHQ-9 for machine learning-based adolescent depression screening. |
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| Συγγραφείς: | Wang C; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Research Center for Brain Health, Pazhou Lab, Guangzhou, 510330, China., Zhang Z; Research Center for Brain Health, Pazhou Lab, Guangzhou, 510330, China., Liang Z; Research Center for Brain Health, Pazhou Lab, Guangzhou, 510330, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China., Yu Z; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China., Yang J; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China., Dai Y; School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China., Zhu X; Research Center for Brain Health, Pazhou Lab, Guangzhou, 510330, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China. Electronic address: zhuxh527@scut.edu.cn. |
| Πηγή: | Journal of affective disorders [J Affect Disord] 2026 Aug 15; Vol. 407, pp. 121758. Date of Electronic Publication: 2026 Apr 07. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7906073 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2517 (Electronic) Linking ISSN: 01650327 NLM ISO Abbreviation: J Affect Disord Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| Ιατρικοί όροι (MeSH): | Speech*/physiology , Depression*/diagnosis , Depressive Disorder*/diagnosis , Machine Learning* , Patient Health Questionnaire*, Mass Screening/methods ; Humans ; Adolescent ; Female ; Cross-Sectional Studies ; Male ; Random Forest |
| Περίληψη: | Background: Traditional screening for adolescent depression primarily relies on self-reported symptoms. However, this method is vulnerable to under-reporting and stigma, which can delay identification and referral. Integrating objective speech biomarkers with machine learning into screening workflows offers a potential adjunctive approach to adolescent depression screening. Methods: A total of 421 adolescents participated in this single-site cross-sectional study, completing speech tasks and the Patient Health Questionnaire-9 (PHQ-9). All participants underwent semi-structured interviews based on the 17-item Hamilton Depression Rating Scale (HAMD-17) and were categorized into control (≤ 7, n = 364) and depression (> 7, n = 57) groups. Following acoustic feature extraction, we performed feature selection via recursive feature elimination with cross-validation. The selected acoustic features and PHQ-9 item scores were concatenated through feature-level multimodal fusion to train and test machine learning models. Model interpretation was conducted using SHAP. Results: Compared to the standard threshold-based PHQ-9 approach, the 30-dimensional multimodal feature set demonstrated superior performance across four machine learning models. Notably, the balanced random forest (BRF) model achieved a balanced accuracy of 0.875 and an AUROC of 0.957 on the training set, with corresponding values of 0.835 and 0.896 on the hold-out test set. Limitations: The main limitations of this study include the absence of longitudinal follow-up and a single-site recruitment design. Conclusions: Machine learning-based multimodal fusion of acoustic features and self-reports enhances adolescent depression screening performance and supports the technical potential for scalable adjunctive screening of current depressive symptoms. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. |
| Contributed Indexing: | Keywords: Adolescent depression screening; Machine learning; Multimodal fusion |
| Entry Date(s): | Date Created: 20260409 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260715 |
| DOI: | 10.1016/j.jad.2026.121758 |
| PMID: | 41956220 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1573-2517 |
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| DOI: | 10.1016/j.jad.2026.121758 |