Applications of Deep Learning in the Identification and Classification of Mental Health Status

Applications of Deep Learning in the Identification and Classification of Mental Health Status | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Applications of Deep Learning in the Identification and Classification of Mental Health Status Xiaolei Wang, Shuo Yang, Xiaohui Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8674020/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This paper addresses the needs of mental health status identification and classification. It tackles the challenges of traditional scale assessments, such as high subjectivity and difficulty in continuous monitoring, as well as the sequential dependence, historical influence decay, and imbalanced category distribution of emotional expression in psychological interview scenarios. To address these issues, a CR-MHA FSA framework integrating hierarchical emotional knowledge and multimodal features is proposed. The model uses Chinese-Roberta-WWM-ext to obtain contextual semantic representations and combines BiLSTM to extract sequential emotional features. A tree-structured hierarchical label space is constructed to characterize the granularity of emotional semantics and label dependence, and multi-head self-attention is used to achieve feature fusion. To better reflect the emotional memory mechanism in interviews, this paper fits the forgetting curve to the emotional word weight allocation function, introducing the decay of historical emotional words over time; simultaneously, Focal Loss is used to reduce the weight of a large number of simple samples to enhance few-sample learning. Experimental results show that on the EMO-DB source task, BiLSTM achieved an average F1 score of 0.817, outperforming LSTM (0.802) and VGG (0.745). In the anomaly recognition induced by emotional stimuli, the F1 score for strong positive stimuli in the question-answering stage was 0.5938, and the F1 score for strong stimuli in the text reading stage was 0.5518. The Pearson similarity between the behavioral entropy depression trend and the total score trend was > 72.2%, reaching a maximum of 85%, with F1 scores for "fear" and "surprise" increasing by 2.41 and 1.37 percentage points, respectively. This paper implements a multimodal mental health detection and assessment system and early warning process, forming a closed loop of "identification—grading—reporting/early warning". BiLSTM CR-MHA FSA multi-head self-attention transformer mental health Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 24 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 01 Feb, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 22 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8674020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587387385,"identity":"8b1ea42e-99c8-4717-b546-61cab9e540a3","order_by":0,"name":"Xiaolei Wang","email":"","orcid":"","institution":"Beijing Police College","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Wang","suffix":""},{"id":587387400,"identity":"b05188fd-15a2-45d9-86d4-d0a1dc9053d3","order_by":1,"name":"Shuo Yang","email":"","orcid":"","institution":"Chinese Health Education Center","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Yang","suffix":""},{"id":587387405,"identity":"fc06fcdc-cdbe-41ea-931a-715ab224b78d","order_by":2,"name":"Xiaohui Feng","email":"data:image/png;base64,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","orcid":"","institution":"Beijing Police College","correspondingAuthor":true,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2026-01-23 02:08:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8674020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8674020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102210083,"identity":"8a430096-730e-4948-9356-d22116e4d957","added_by":"auto","created_at":"2026-02-09 12:16:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730051,"visible":true,"origin":"","legend":"","description":"","filename":"ApplicationsofDeepLearningintheIdentificationandClassificationofMentalHealthStatus.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8674020/v1_covered_845f5508-81e4-4537-a170-4d21d5eabbb8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applications of Deep Learning in the Identification and Classification of Mental Health Status","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BiLSTM, CR-MHA FSA, multi-head self-attention, transformer, mental health","lastPublishedDoi":"10.21203/rs.3.rs-8674020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8674020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper addresses the needs of mental health status identification and classification. It tackles the challenges of traditional scale assessments, such as high subjectivity and difficulty in continuous monitoring, as well as the sequential dependence, historical influence decay, and imbalanced category distribution of emotional expression in psychological interview scenarios. To address these issues, a CR-MHA FSA framework integrating hierarchical emotional knowledge and multimodal features is proposed. The model uses Chinese-Roberta-WWM-ext to obtain contextual semantic representations and combines BiLSTM to extract sequential emotional features. A tree-structured hierarchical label space is constructed to characterize the granularity of emotional semantics and label dependence, and multi-head self-attention is used to achieve feature fusion. To better reflect the emotional memory mechanism in interviews, this paper fits the forgetting curve to the emotional word weight allocation function, introducing the decay of historical emotional words over time; simultaneously, Focal Loss is used to reduce the weight of a large number of simple samples to enhance few-sample learning. Experimental results show that on the EMO-DB source task, BiLSTM achieved an average F1 score of 0.817, outperforming LSTM (0.802) and VGG (0.745). In the anomaly recognition induced by emotional stimuli, the F1 score for strong positive stimuli in the question-answering stage was 0.5938, and the F1 score for strong stimuli in the text reading stage was 0.5518. The Pearson similarity between the behavioral entropy depression trend and the total score trend was \u0026gt;\u0026thinsp;72.2%, reaching a maximum of 85%, with F1 scores for \"fear\" and \"surprise\" increasing by 2.41 and 1.37 percentage points, respectively. 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