[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126397-en":3,"doc-seo-126397-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126397,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Depression diagnosis from patient interviews using multimodal machine learning - Research article","Depression diagnosis from patient interviews using multimodal machine learning investigates how speech, language, and structured clinical information can function as objective markers for clinical assessment. The approach trains separate models for each modality and combines them via multimodal fusion to reflect real-world psychiatric evaluation complexity. Performance is validated with established metrics, followed by calibration and decision-analytic analyses to estimate potential clinical utility. Results show improved diagnostic accuracy and stronger calibration than single-modality baselines.","TYPE Original Research PUBLISHED 27 November 2025 DOI 10.3389/fpsyt.2025.1694762  \nOPEN ACCESS  \nEDITED BY  \nVanessa Panaite,  \nJames A Haley Veteran’s Hospital, United States  \nREVIEWED BY  \nDezon Finch,  \nHines VA Medical Center, United States Shanliang Yang,  \nShandong University of Technology, China  \n*CORRESPONDENCE  \nJuan Miguel Lopez Alcaraz  \n[juan.lopez.alcaraz@uol.de](juan.lopez.alcaraz@uol.de)  \nRECEIVED 28 August 2025  \nREVISED 08 October 2025  \nACCEPTED 07 November 2025  \nPUBLISHED 27 November 2025  \nCITATION  \nWeber J, Weber M and Lopez Alcaraz JM (2025) Depression diagnosis from patient interviews using multimodal machine learning.  \nFront. Psychiatry 16:1694762 .  \ndoi: 10.3389/fpsyt.2025.1694762  \nCOPYRIGHT  \n© 2025 Weber, Weber and Lopez Alcaraz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDepression diagnosis from patient interviews using multimodal machine learning  \nJana Weber, Marcel Weber and Juan Miguel Lopez Alcaraz* AI4Health Division, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany  \nBackground: Depression is a major public health concern, affecting an estimated five percent of the global population. Early and accurate diagnosis is essential to initiate effective treatment, yet recognition remains challenging in many clinical contexts. Speech, language, and behavioral cues collected during patient interviews may provide objective markers that support clinical assessment. Methods: We developed a diagnostic approach that integrates features derived from patient interviews, including speech patterns, linguistic characteristics, and structured clinical information. Separate models were trained for each modality and subsequently combined through multimodal fusion to reflect the complexity of real-world psychiatric assessment. Model validity was assessed with established performance metrics, and further evaluated using calibration and decision-analytic approaches to estimate potential clinical utility.  \nResults: The multimodal model achieved superior diagnostic accuracy compared to single-modality models, with an AUROC of 0 . 88 and a macro F1-score of 0 .75. Importantly, the fused model demonstrated good calibration and offered higher net clinical benefit compared to baseline strategies, highlighting its potential to assist clinicians in identifying patients with depression more reliably. Conclusion: Multimodal analysis of patient interviews using machine learning may serve as a valuable adjunct to psychiatric evaluation. By combining speech, language, and clinical features, this approach provides a robust framework that could enhance early detection of depressive disorders and support evidencebased decision-making in mental healthcare.  \nKEYWORDS  \ndepression diagnosis, digital biomarkers, multimodal analysis, machine learning, deep learning, clinical decision support  \n1 Introduction  \n1.1 Depression as a problem worldwide  \nDepression represents a significant public health issue, impacting approximately 322 million individuals worldwide and accounting for 7.5% of total years lived with disability Organization et al. (1). Untreated depression is associated with impaired quality of life, increased risk ofcomorbidities, and elevated mortality Voros et al. (2). Early and accurate diagnosis is essential to  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \nWeber et al. 10.3389/fpsyt.2025.1694762  \ninitiate effective treatment, yet recognition remains challenging in many clinical contexts due to subtle symptom presentation, variability across populations, clinical judgment, and commo","cbCail5LeZu5LtyB","https://ap.wps.com/l/cbCail5LeZu5LtyB","pdf",1630256,6,1,10,"English","en",105,"# Background\n## Depression as a problem worldwide\n## Machine learning in neuropsychiatry\n## Speech and text for depression detection\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What data modalities are integrated in the multimodal depression diagnosis approach?\",\"answer\":\"The method integrates features derived from patient interviews, including speech patterns, linguistic characteristics, and structured clinical information.\"},{\"question\":\"How do the models combine information from different modalities?\",\"answer\":\"Separate models are trained for each modality and then combined through multimodal fusion to capture the complexity of psychiatric assessment.\"},{\"question\":\"What do the reported results indicate about diagnostic performance?\",\"answer\":\"The fused multimodal model achieves higher diagnostic accuracy than single-modality models, with an AUROC of 0.88 and a macro F1-score of 0.75, along with good calibration and higher net clinical benefit.\"}]","Depression diagnosis from patient interviews using multimodal machine learning - 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