{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:34:26Z","timestamp":1777952066226,"version":"3.51.4"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":31,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.01.078","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:30:19Z","timestamp":1774035019000},"page":"672-681","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Analyzing the Impact of Deep Learning and Machine Learning on Mental Disorders: a Bibliometric Study"],"prefix":"10.1016","volume":"275","author":[{"given":"Abdallah M.A.","family":"Al-Tarawneh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda","family":"Alkhawaja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwan","family":"Salameh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loai","family":"Al Olimat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasha","family":"Mashal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mona ahmed","family":"Almadhoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.01.078_bib1","doi-asserted-by":"crossref","unstructured":"Friis-Healy, E. A., Nagy, G. A., & Kollins, S. H. (2021). It is time to REACT: opportunities for digital mental health apps to reduce mental health disparities in racially and ethnically minoritized groups. JMIR mental health, 8(1), e25456.\u200f","DOI":"10.2196\/25456"},{"key":"10.1016\/j.procs.2026.01.078_bib2","doi-asserted-by":"crossref","unstructured":"D\u2019Alfonso, S. (2020). AI in mental health. Current opinion in psychology, 36, 112-117.\u200f","DOI":"10.1016\/j.copsyc.2020.04.005"},{"key":"10.1016\/j.procs.2026.01.078_bib3","doi-asserted-by":"crossref","unstructured":"Hang, C. N., Yu, P. D., Chen, S., Tan, C. W., & Chen, G. (2023). MEGA: machine learning-enhanced graph analytics for infodemic risk management. IEEE Journal of Biomedical and Health Informatics, 27(12), 6100-6111.\u200f","DOI":"10.1109\/JBHI.2023.3314632"},{"issue":"6","key":"10.1016\/j.procs.2026.01.078_bib4","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.17507\/tpls.1406.31","article-title":"Embedding artificial intelligent applications in higher educational institutions to improve students\u2019 pronunciation performance.","volume":"14","author":"Al-Shallakh","year":"2024","journal-title":"Theory and Practice in Language Studies"},{"key":"10.1016\/j.procs.2026.01.078_bib5","doi-asserted-by":"crossref","unstructured":"Garg, M., Saxena, C., Naseem, U., & Dorr, B. J. (2023). NLP as a lens for causal analysis and perception mining to infer mental health on social media. arXiv preprint arXiv:2301.11004.\u200f","DOI":"10.36227\/techrxiv.21972974"},{"key":"10.1016\/j.procs.2026.01.078_bib6","doi-asserted-by":"crossref","unstructured":"Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649-685.\u200f","DOI":"10.1017\/S1351324916000383"},{"issue":"1","key":"10.1016\/j.procs.2026.01.078_bib7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s41746-022-00589-7","article-title":"Natural language processing applied to mental illness detection: a narrative review.","volume":"5","author":"Zhang","year":"2022","journal-title":"NPJ digital medicine"},{"issue":"13","key":"10.1016\/j.procs.2026.01.078_bib8","doi-asserted-by":"crossref","DOI":"10.3390\/math12131926","article-title":"Mental-health: an NLP-based system for detecting depression levels through user comments on twitter (X).","volume":"12","author":"Salas-Z\u00e1rate","year":"2024","journal-title":"Mathematics"},{"issue":"1","key":"10.1016\/j.procs.2026.01.078_bib9","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41746-020-0233-7","article-title":"Methods in predictive techniques for mental health status on social media: a critical review","volume":"3","author":"Chancellor","year":"2020","journal-title":"NPJ digital medicine"},{"key":"10.1016\/j.procs.2026.01.078_bib10","doi-asserted-by":"crossref","unstructured":"Sun, J., Lu, T., Shao, X., Han, Y., Xia, Y., Zheng, Y., ... & Lu, L. (2025). Practical AI application in psychiatry: historical review and future directions. Molecular Psychiatry, 1-10.\u200f","DOI":"10.1038\/s41380-025-03072-3"},{"key":"10.1016\/j.procs.2026.01.078_bib11","doi-asserted-by":"crossref","unstructured":"Abd-Alrazaq, A., Alhuwail, D., Schneider, J., Toro, C. T., Ahmed, A., Alzubaidi, M., ... & Househ, M. (2022). The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. Npj Digital Medicine,5(1), 87.","DOI":"10.1038\/s41746-022-00631-8"},{"key":"10.1016\/j.procs.2026.01.078_bib12","doi-asserted-by":"crossref","unstructured":"Jena, R., Muley, U., Gandhi, T., Prajapati, A., Parmar, B., Trivedi, J., & Dave, V. (2025). Leveraging Deep Learning For Automated Detection Of Mental Disorders: A Survey And Future Directions. American Journal of Psychiatric Rehabilitation, 28(1), 583-589.\u200f","DOI":"10.69980\/ajpr.v28i1.149"},{"key":"10.1016\/j.procs.2026.01.078_bib13","doi-asserted-by":"crossref","DOI":"10.2174\/0117450179315688240607052117","article-title":"Machine learning techniques to predict mental health diagnoses: A systematic literature review","volume":"20","author":"Madububambachu","year":"2024","journal-title":"Clinical Practice and Epidemiology in Mental Health: CP & EMH"},{"key":"10.1016\/j.procs.2026.01.078_bib14","unstructured":"Eke, C. I., Al-Shamayleh, A. S., Phiri, M., Maswadi, K., Kwaghtyo, D. K., Mulenga, M., & Iyidobi, C. J. (2025). Machine Learning Based Mobile Big Data Analytics: State-of-the-art Applications, Taxonomy, Challenges and Future Research Directions. Nigerian Journal of Technological Development, 22(4), 65-89.\u200f"},{"key":"10.1016\/j.procs.2026.01.078_bib15","doi-asserted-by":"crossref","unstructured":"Amanat, A., Rizwan, M., Javed, A. R., Abdelhaq, M., Alsaqour, R., Pandya, S., & Uddin, M. (2022). Deep learning for depression detection from textual data. Electronics, 11(5), 676.\u200f","DOI":"10.3390\/electronics11050676"},{"key":"10.1016\/j.procs.2026.01.078_bib16","doi-asserted-by":"crossref","unstructured":"Zhao, K., Duka, B., Xie, H., Oathes, D. J., Calhoun, V., & Zhang, Y. (2022). A dynamic graph convolutional neural network framework reveals new insights into connectome dysfu","DOI":"10.1016\/j.neuroimage.2021.118774"},{"key":"10.1016\/j.procs.2026.01.078_bib17","doi-asserted-by":"crossref","unstructured":"Huang, Z. A., Hu, Y., Liu, R., Xue, X., Zhu, Z., Song, L., & Tan, K. C. (2022). Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans. IEEE Transactions on Biomedical Engineering, 70(4), 1137-1149.\u200f","DOI":"10.1109\/TBME.2022.3210940"},{"issue":"4","key":"10.1016\/j.procs.2026.01.078_bib18","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TMI.2021.3051604","article-title":"A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity","volume":"40","author":"Yao","year":"2021","journal-title":"IEEE transactions on medical imaging"},{"key":"10.1016\/j.procs.2026.01.078_bib19","doi-asserted-by":"crossref","unstructured":"Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology, 46(1), 176-190.\u200f","DOI":"10.1038\/s41386-020-0767-z"},{"key":"10.1016\/j.procs.2026.01.078_bib20","doi-asserted-by":"crossref","unstructured":"Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depression: diagnosis, insights, and research directions. Electronics, 11(7), 1111.\u200f","DOI":"10.3390\/electronics11071111"},{"key":"10.1016\/j.procs.2026.01.078_bib21","doi-asserted-by":"crossref","unstructured":"Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Robinson, J. E., Sridharan, S., Johnston, P. J., ... & Fookes, C. (2020). Identification of children at risk of schizophrenia via deep learning and EEG responses. IEEE journal of biomedical and health informatics, 25(1), 69-76.\u200f","DOI":"10.1109\/JBHI.2020.2984238"},{"key":"10.1016\/j.procs.2026.01.078_bib22","doi-asserted-by":"crossref","unstructured":"Bagherzadeh, S., Shahabi, M. S., & Shalbaf, A. (2022). Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Computers in Biology and Medicine, 146, 105570.\u200f","DOI":"10.1016\/j.compbiomed.2022.105570"},{"key":"10.1016\/j.procs.2026.01.078_bib23","doi-asserted-by":"crossref","unstructured":"Cortes-Briones, J. A., Tapia-Rivas, N. I., D\u2019Souza, D. C., & Estevez, P. A. (2022). Going deep into schizophrenia with artificial intelligence. Schizophrenia research, 245, 122-140.\u200f","DOI":"10.1016\/j.schres.2021.05.018"},{"key":"10.1016\/j.procs.2026.01.078_bib24","doi-asserted-by":"crossref","unstructured":"Rivera, M. J., Teruel, M. A., Mate, A., & Trujillo, J. (2022). Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artificial Intelligence Review, 55(2), 1209-1251.\u200f","DOI":"10.1007\/s10462-021-09986-y"},{"key":"10.1016\/j.procs.2026.01.078_bib25","first-page":"1","article-title":"\"Analyzing the Challenges Facing Digital Mental Health (DMH) Between Aspiration and Reality,\"","author":"Tassnim","year":"2024","journal-title":"2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates"},{"key":"10.1016\/j.procs.2026.01.078_bib26","first-page":"1","article-title":"\"Exploring the Role of Artificial Intelligence Applications in Developing Clinical Psychological Research: Implications and Future Aspirations,\"","author":"Abunasrieh","year":"2024","journal-title":"2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates"},{"issue":"1","key":"10.1016\/j.procs.2026.01.078_bib27","first-page":"41","article-title":"Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis. Journal of research in medical sciences:","volume":"19","author":"Zare-Farashbandi","year":"2014","journal-title":"the official journal of Isfahan University of Medical Sciences"},{"key":"10.1016\/j.procs.2026.01.078_bib28","series-title":"Hotspots and Trends in Research on Mental Health of the Rural Elderly: A Bibliometric Analysis Using CiteSpace. In Healthcare (Vol. 13, No. 3, p. 209)","author":"Hao","year":"2025"},{"issue":"11","key":"10.1016\/j.procs.2026.01.078_bib29","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.3390\/diagnostics15111412","article-title":"Explainable Machine Learning in the Prediction of Depression.","volume":"15","author":"Mimikou","year":"2025","journal-title":"Diagnostics"},{"key":"10.1016\/j.procs.2026.01.078_bib30","doi-asserted-by":"crossref","first-page":"100125","DOI":"10.1016\/j.pmip.2024.100125","article-title":"The use of machine learning and deep learning models in detecting depression on social media: A systematic literature review.","volume":"45","author":"Gadzama","year":"2024","journal-title":"Personalized Medicine in Psychiatry"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926000785?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926000785?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:25:35Z","timestamp":1777893935000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926000785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":30,"alternative-id":["S1877050926000785"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.01.078","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Analyzing the Impact of Deep Learning and Machine Learning on Mental Disorders: a Bibliometric Study","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.01.078","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}