Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework</title> <meta name="description" content="Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework"> <meta name="keywords" content="Artificial intelligence, depression detection, facial emotion recognition, natural language processing, mental disorder."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework"> <meta name="citation_author" content="Raymond Xu"> <meta name="citation_author" content="Ashley Hua"> <meta name="citation_author" content="Andrew Wang"> <meta name="citation_author" content="Yuru Lin"> <meta name="citation_publication_date" content="2021/05/02"> <meta name="citation_journal_title" content="International Journal of Psychological and Behavioral Sciences"> <meta name="citation_volume" content="15"> <meta name="citation_issue" content="6"> <meta name="citation_firstpage" content="219"> <meta name="citation_lastpage" content="225"> <meta name="citation_pdf_url" content="https://publications.waset.org/10012110/pdf"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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with a Psychiatric and AI Interdisciplinary Multimodal Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Raymond%20Xu">Raymond Xu</a>, <a href="https://publications.waset.org/search?q=Ashley%20Hua"> Ashley Hua</a>, <a href="https://publications.waset.org/search?q=Andrew%20Wang"> Andrew Wang</a>, <a href="https://publications.waset.org/search?q=Yuru%20Lin"> Yuru Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.</p> <iframe src="https://publications.waset.org/10012110.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=depression%20detection" title=" depression detection"> depression detection</a>, <a href="https://publications.waset.org/search?q=facial%20emotion%20recognition" title=" facial emotion recognition"> facial emotion recognition</a>, <a href="https://publications.waset.org/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/search?q=mental%20disorder." title=" mental disorder."> mental disorder.</a> </p> <a href="https://publications.waset.org/10012110/early-depression-detection-for-young-adults-with-a-psychiatric-and-ai-interdisciplinary-multimodal-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012110/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012110/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012110/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012110/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012110/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012110/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012110/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012110/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012110/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012110/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012110.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1179</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] “Depression.” https://www.who.int/news-room/fact-sheets/detail/depression (accessed Apr. 29, 2021). <br>[2] S. Avenevoli, J. Swendsen, J.-P. He, M. Burstein, and K. Merikangas, “Major Depression in the National Comorbidity Survey- Adolescent Supplement: Prevalence, Correlates, and Treatment,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 54, Oct. 2014, doi: 10.1016/j.jaac.2014.10.010. <br>[3] R. Feintzeig, “Is It OK to Reveal Your Anxiety or Depression to Your Boss?,” Wall Street Journal, Sep. 13, 2020. <br>[4] “Depression,” Cmu.edu, 2021. https://www-sciencedirect-com.proxy.library.cmu.edu/science/article/pii/S0140673618319482?via%3Dihub (accessed Apr. 29, 2021). <br>[5] P. Arora and P. Arora, “Mining Twitter Data for Depression Detection,” IEEE Xplore, 2019. https://ieeexplore.ieee.org/document/8938353/authors#authors (accessed Apr. 29, 2021). <br>[6] S. Moon, L. Neves, and V. Carvalho, “Multimodal Named Entity Recognition for Short Social Media Posts,” arXiv:1802.07862 (cs), Feb. 2018, Accessed: Apr. 29, 2021. (Online). Available: https://arxiv.org/abs/1802.07862. <br>[7] J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, doi: 10.18653/v1/p18-1031. <br>[8] A. Benton, M. Mitchell, and D. Hovy, “Multitask Learning for Mental Health Conditions with Limited Social Media Data,” ACLWeb, Apr. 01, 2017. https://www.aclweb.org/anthology/E17-1015/ (accessed Apr. 29, 2021). <br>[9] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv.org, 2014. https://arxiv.org/abs/1409.1556. <br>[10] J. Chung, C. Gulcehre, K. Cho, and Y. 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Escobar-Viera, et al. “Emotional support from social media and face-to-face relationships: Associations with depression risk among young adults” (Online) Available: https://www.tandfonline.com/doi/full/10.1080/09638237.2019.1581357?scroll=top&needAccess=true <br>[15] Y. Ophir, C. S. C. Asterhan, and B. B. Schwarz, “Unfolding the notes from the walls: Adolescents’ depression manifestations on Facebook,” Computers in Human Behavior, vol. 72, pp. 96–107, Jul. 2017, doi: 10.1016/j.chb.2017.02.013. <br>[16] K. Harvey, “Disclosures of depression,” International Journal of Corpus Linguistics, vol. 17, no. 3, pp. 349–379, Dec. 2012, doi: 10.1075/ijcl.17.3.03har. <br>[17] R. Dryden-Edwards, “Teen Depression Facts, Treatment, Symptoms, Statistics & Tests,” MedicineNet, 2019. https://www.medicinenet.com/teen_depression/article.htm. <br>[18] Thomas, Lauren. What is a longitudinal study?. (Online) Available: https://www.scribbr.com/methodology/longitudinal-study <br>[19] B. L. Hankin, L. Y. Abramson, T. E. Moffitt, P. A. Silva, R. Mcgee, and K. E. Angell, “Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study.,” Journal of Abnormal Psychology, vol. 107, no. 1, pp. 128–140, 1998. <br>[20] S. E. Gilman, E. Sucha, M. Kingsbury, N. J. Horton, J. M. Murphy, and I. Colman, “Depression and mortality in a longitudinal study: 1952–2011,” CMAJ, 23-Oct-2017. (Online). Available: https://www.cmaj.ca/content/189/42/E1304.short. (Accessed: 17-May-2021). <br>[21] “APA PsycNet,” psycnet.apa.org. https://psycnet.apa.org/record/2013-14907-000. <br>[22] “NIMH» Depression Basics,” www.nimh.nih.gov. https://www.nimh.nih.gov/health/publications/depression/. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework