{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T01:21:13Z","timestamp":1786584073973,"version":"build-2736575974"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T00:00:00Z","timestamp":1642896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day.<\/jats:p>","DOI":"10.3390\/s22030846","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:36:27Z","timestamp":1642970187000},"page":"846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7911-2707","authenticated-orcid":false,"given":"Ridha","family":"Mezzi","sequence":"first","affiliation":[{"name":"Military Academy of Fondouk Jedid, Nabeul 8012, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1721-4510","authenticated-orcid":false,"given":"Aymen","family":"Yahyaoui","sequence":"additional","affiliation":[{"name":"Military Academy of Fondouk Jedid, Nabeul 8012, Tunisia"},{"name":"SERCOM Laboratory, University of Carthage, Carthage 1054, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9261-8945","authenticated-orcid":false,"given":"Mohamed Wassim","family":"Krir","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Military Hospital of Instruction of Tunis, Mont Fleury, Tunis 1008, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"},{"name":"RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-7423","authenticated-orcid":false,"given":"Anis","family":"Koubaa","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e10652","DOI":"10.2196\/10652","article-title":"Real-world technology use among people with mental illnesses: Qualitative study","volume":"5","author":"Noel","year":"2018","journal-title":"JMIR Ment. Health"},{"key":"ref_2","first-page":"22","article-title":"The Mini-International Neuropsychiatric Interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10","volume":"59","author":"Sheehan","year":"1998","journal-title":"J. Clin. Psychiatry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e15708","DOI":"10.2196\/15708","article-title":"Machine learning and natural language processing in mental health: Systematic review","volume":"23","author":"Haralambous","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_4","unstructured":"Devlin, J., and Chang, M.W. (2018). Open sourcing BERT: State-of-the-art pre-training for natural language processing. Google AI Blog, Available online: https:\/\/ai.googleblog.com\/2018\/11\/open-sourcing-bert-state-of-art-pre.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","article-title":"Transformer models for text-based emotion detection: A review of BERT-based approaches","volume":"54","author":"Acheampong","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.neucom.2021.05.103","article-title":"An introduction to deep learning in natural language processing: Models, techniques, and tools","volume":"470","author":"Lauriola","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/jclp.22031","article-title":"Mental health professionals\u2019 natural taxonomies of mental disorders: Implications for the clinical utility of the ICD-11 and the DSM-5","volume":"69","author":"Reed","year":"2013","journal-title":"J. Clin. Psychol."},{"key":"ref_8","unstructured":"(2021). Mental Health, Harvard-Health-Publishing."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.jad.2011.12.036","article-title":"Depression as a disease of modernity: Explanations for increasing prevalence","volume":"140","author":"Hidaka","year":"2012","journal-title":"J. Affect. Disord."},{"key":"ref_10","unstructured":"World-Health-Organization (2020). Improving the Mental and Brain Health of Children and Adolescents, WHO."},{"key":"ref_11","unstructured":"WHO (2017). Depression and Other Common Mental Disorders: Global Health Estimates, World Health Organization. Technical Report."},{"key":"ref_12","unstructured":"Metz, C., and Smith, C.S. (The New York Times, 2019). Warnings of a Dark Side to A.I. in Health Care, The New York Times."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17331","DOI":"10.1109\/ACCESS.2020.2964952","article-title":"Ensemble methods for instance-based arabic language authorship attribution","volume":"8","author":"Saeed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Conneau, A., Schwenk, H., Barrault, L., and Lecun, Y. (2016). Very Deep Convolutional Networks for Natural Language Processing. arXiv.","DOI":"10.18653\/v1\/E17-1104"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.inffus.2016.10.004","article-title":"A review of natural language processing techniques for opinion mining systems","volume":"36","author":"Sun","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1126\/science.aaa8685","article-title":"Advances in natural language processing","volume":"349","author":"Hirschberg","year":"2015","journal-title":"Science"},{"key":"ref_17","unstructured":"Polignano, M., Basile, P., De Gemmis, M., Semeraro, G., and Basile, V. (2019, January 13\u201315). Alberto: Italian BERT language understanding model for NLP challenging tasks based on tweets. Proceedings of the 6th Italian Conference on Computational Linguistics, CLiC-it 2019, Bari, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3103\/S0147688210020097","article-title":"Chatterbots","volume":"37","author":"Deryugina","year":"2010","journal-title":"Sci. Tech. Inf. Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/365153.365168","article-title":"ELIZA\u2014A computer program for the study of natural language communication between man and machine","volume":"9","author":"Weizenbaum","year":"1966","journal-title":"Commun. ACM"},{"key":"ref_20","unstructured":"Saxon, W. (The New York Times, 2001). Kenneth Colby, 81, Psychiatrist Expert in Artificial Intelligence (Published 2001), The New York Times."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1023\/A:1011288000451","article-title":"Turing test: 50 years later","volume":"10","author":"Saygin","year":"2000","journal-title":"Minds Mach."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e26771","DOI":"10.2196\/26771","article-title":"Acceptability and Effectiveness of Artificial Intelligence Therapy for Anxiety and Depression (Youper): Longitudinal Observational Study","volume":"23","author":"Mehta","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e7785","DOI":"10.2196\/mental.7785","article-title":"Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial","volume":"4","author":"Fitzpatrick","year":"2017","journal-title":"JMIR Ment. Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.beth.2018.12.003","article-title":"Pilot randomized trial of a self-help behavioral activation mobile app for utilization in primary care","volume":"50","author":"Dahne","year":"2019","journal-title":"Behav. Ther."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e12106","DOI":"10.2196\/12106","article-title":"An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study","volume":"6","author":"Inkster","year":"2018","journal-title":"JMIR mHealth uHealth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1037\/a0034559","article-title":"Artificial intelligence in psychological practice: Current and future applications and implications","volume":"45","author":"Luxton","year":"2014","journal-title":"Prof. Psychol. Res. Pract."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/00001199-199712000-00002","article-title":"Virtual reality and cognitive rehabilitation: A brief review of the future","volume":"12","author":"Rizzo","year":"1997","journal-title":"J. Head Trauma Rehabil."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1007\/s10880-011-9247-2","article-title":"Virtual reality goes to war: A brief review of the future of military behavioral healthcare","volume":"18","author":"Rizzo","year":"2011","journal-title":"J. Clin. Psychol. Med. Settings"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1176\/appi.ap.33.5.413","article-title":"Computer simulation and virtual reality in the diagnosis and treatment of psychiatric disorders","volume":"33","author":"Gorrindo","year":"2009","journal-title":"Acad. Psychiatry"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.cpr.2004.04.001","article-title":"Virtual reality exposure therapy of anxiety disorders: A review","volume":"24","author":"Krijn","year":"2004","journal-title":"Clin. Psychol. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/jts.20574","article-title":"Effectiveness of virtual reality exposure therapy for active duty soldiers in a military mental health clinic","volume":"24","author":"Reger","year":"2011","journal-title":"J. Trauma. Stress"},{"key":"ref_32","unstructured":"McCarthy, J. (1960). Programs with Common Sense, RLE and MIT Computation Center."},{"key":"ref_33","unstructured":"Finlay, P.N. (1994). Introducing Decision Support Systems, Blackwell Pub."},{"key":"ref_34","unstructured":"Boulila, W., Farah, I.R., Saheb Ettabaa, K., Solaiman, B., and Gh\u00e9zala, H.B. (2010). Spatio-Temporal Modeling for Knowledge Discovery in Satellite Image Databases. CORIA, 35\u201349."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Al-Sarem, M., Alsaeedi, A., Saeed, F., Boulila, W., and AmeerBakhsh, O. (2021). A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs. Appl. Sci., 11.","DOI":"10.3390\/app11177940"},{"key":"ref_36","unstructured":"Buchanan, B.G., and Shortliffe, E.H. (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley Pub. Co."},{"key":"ref_37","unstructured":"Shortliffe, E. (2012). Computer-Based Medical Consultations: MYCIN, Elsevier."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101325","DOI":"10.1016\/j.ecoinf.2021.101325","article-title":"A novel CNN-LSTM-based approach to predict urban expansion","volume":"64","author":"Boulila","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Varone, G., Boulila, W., Lo Giudice, M., Benjdira, B., Mammone, N., Ieracitano, C., Dashtipour, K., Neri, S., Gasparini, S., and Morabito, F.C. (2022). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors, 22.","DOI":"10.3390\/s22010129"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_41","first-page":"63","article-title":"Diagnosing Parkinson\u2019s by using artificial neural networks and support vector machines","volume":"9","author":"Gil","year":"2009","journal-title":"Glob. J. Comput. Sci. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.neurobiolaging.2010.04.022","article-title":"Boosting power for clinical trials using classifiers based on multiple biomarkers","volume":"31","author":"Kohannim","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Masri, R.Y., and Jani, H.M. (2012, January 12\u201314). Employing artificial intelligence techniques in mental health diagnostic expert system. Proceedings of the International Conference on Computer & Information Science (ICCIS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCISci.2012.6297296"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1111\/j.1365-2710.2011.01327.x","article-title":"The potential for intelligent decision support systems to improve the quality and consistency of medication reviews","volume":"37","author":"Bindoff","year":"2012","journal-title":"J. Clin. Pharm. Ther."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.artmed.2012.04.005","article-title":"Inconsistency as a diagnostic tool in a society of intelligent agents","volume":"55","author":"McShane","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"ref_46","unstructured":"Meeker, D., Cerully, J.L., Johnson, M., Iyer, N., Kurz, J., and Scharf, D.M. (2016). SimCoach Evaluation: A Virtual Human Intervention to Encourage Service-Member Help-Seeking for Posttraumatic Stress Disorder and Depression, Rand National Defense Research Inst.. Rand Health Quarterly."},{"key":"ref_47","unstructured":"Statista (2021, May 23). Speech-to-Text Transcript Accuracy Rate among Leading Companies Worldwide in 2020. Available online: https:\/\/www.statista.com\/statistics\/1133833\/speech-to-text-transcript-accuracy-rate-among-leading-companies\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:06:07Z","timestamp":1760133967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,23]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030846"],"URL":"https:\/\/doi.org\/10.3390\/s22030846","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,23]]}}}