Artificial intelligence in the detection and treatment of depressive disorders: a narrative review of literature

References

• Abdul-Kader, S. A., & Woods, J. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(1), 72–80. https://thesai.org/Downloads/Volume6No7/Paper_12-Survey_on_Chatbot_Design_Techniques_in_Speech_Conversation_Systems.pdf
   Google Scholar
• Ahmed, A., Hassan, A., Aziz, S., Abd-Alrazaq, A. A., Ali, N., Alzubaidi, M., Al-Thani, D., Elhusein, B., Siddig, M. A., Ahmed, M., & Househ, M. (2023). Chatbot features for anxiety and depression: A scoping review. Health Informatics Journal, 29(1), 14604582221146719. https://doi.org/10.1177/14604582221146719
   Web of Science ®Google Scholar
• Allen, S. (2020). Artificial intelligence and the future of psychiatry. IEEE Pulse, 11(3), 2–6. https://doi.org/10.1109/MPULS.2020.2993657
   PubMed Web of Science ®Google Scholar
• Amanollahi, M., Jameie, M., Looha, M. A., Basti, F. A., Cattarinussi, G., Moghaddam, H. S., Di Camillo, F., Akhondzadeh, S., Pigoni, A., Sambataro, F., Brambilla, P., & Delvecchio, G. (2024). Machine learning applied to prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review. Journal of Affective Disorders, 361(24), 778–797. https://doi.org/10.1016/j.jad.2024.06.061
   PubMedGoogle Scholar
• Andrews, J. A., Harrison, R. F., Brown, L. J. E., MacLean, L. M., Hwang, F., Smith, T., Williams, E. A., Timon, C., Adlam, T., Khadra, H., & Astell, A. J. (2017). Using the NANA toolkit at home to predict older adults’ future depression. Journal of Affective Disorders, 213, 187–190. https://doi.org/10.1016/j.jad.2017.02.019
   PubMed Web of Science ®Google Scholar
• Anshu, M., & Rajni, J. (2022). Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing, 130, 109713. https://doi.org/10.1016/j.asoc.2022.109713
   Web of Science ®Google Scholar
• Ayano, G., Demelash, S., Yohannes, Z., Haile, K., Tulu, M., Assefa, D., Tesfaye, A., Haile, K., Solomon, M., Chaka, A., & Tsegay, L. (2021). Misdiagnosis, detection rate, and associated factors of severe psychiatric disorders in specialized psychiatry centers in Ethiopia. Annals of General Psychiatry, 20(1), 10. https://doi.org/10.1186/s12991-021-00333-7
   PubMed Web of Science ®Google Scholar
• Bachmann, S. (2018). Epidemiology of suicide and the psychiatric perspective. International Journal of Environmental Research and Public Health, 15(7), 1425. https://doi.org/10.3390/ijerph15071425
   PubMed Web of Science ®Google Scholar
• Bao, Z., Zhao, X., Li, J., Zhang, G., Wu, H., Ning, Y., Li, M. D., & Yang, Z. (2021). Prediction of repeated-dose ­intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach. Journal of Psychiatric Research, 138, 284–290. https://doi.org/10.1016/j.jpsychires.2021.04.014
   PubMed Web of Science ®Google Scholar
• Bendig, E., Erb, B., Schulze-Thuesing, L., & Baumeister, H. (2022). The next generation: Chatbots in clinical psychology and psychotherapy to foster mental health—A scoping review. Verhaltenstherapie, 32(Suppl. 1), 64–76. https://doi.org/10.1159/000501812
   Google Scholar
• Berry, D. M. (2018). Weizenbaum, ELIZA and the end of human reason. In M. Baranovska & S. Höltgen (Eds.), Hello, I’m Eliza: Fünfzig Jahre Gespräche mit Computern [Hello, I’m Eliza: Fifty Years of Conversations with Computers] (1st ed., pp. 53–70). Projekt Verlag.
   Google Scholar
• Bhadra, S., & Kumar, C. J. (2022). An insight into diagnosis of depression using machine learning techniques: A systematic review. Current Medical Research and Opinion, 38(5), 749–771. https://doi.org/10.1080/03007995.2022.2038487
   PubMed Web of Science ®Google Scholar
• Bhugra, D., Liebrenz, M., Ventriglio, A., Ng, R., Javed, A., Kar, A., Chumakov, E., Moura, H., Tolentino, E., Gupta, S., Ruiz, R., Okasha, T., Chisolm, M. S., Castaldelli-Maia, J., Torales, J., & Smith, A. (2024). World psychiatric association-asian journal of psychiatry commission on public mental health. Asian Journal of Psychiatry, 98, 104105. Advance online publication https://doi.org/10.1016/j.ajp.2024.104105
   PubMed Web of Science ®Google Scholar
• Bobo, W. V., Van Ommeren, B., & Athreya, A. P. (2022). Machine learning, pharmacogenomics, and clinical psychiatry: Predicting antidepressant response in patients with major depressive disorder. Expert Review of Clinical Pharmacology, 15(8), 927–944. PMID: 35968639 https://doi.org/10.1080/17512433.2022.2112949
   PubMed Web of Science ®Google Scholar
• Bresó, A., Martínez-Miranda, J., Botella, C., Baños, R. M., & García-Gómez, J. M. (2016). Usability and acceptability assessment of an empathic virtual agent to prevent major depression. Expert Systems, 33(4), 297–312. https://doi.org/10.1111/exsy.12151
   Web of Science ®Google Scholar
• Brown, E., Kaiser, R. M., & Gellis, Z. D. (2007). Screening and assessment of late-life depression in home healthcare: Issues and challenges. Annals of Long Term Care, 15(10), 27.
   Google Scholar
• Cearns, M., Hahn, T., & Baune, B. T. (2019). Recommendations and future directions for supervised machine learning in psychiatry. Translational Psychiatry, 9(1), 271. https://doi.org/10.1038/s41398-019-0607-2
   PubMedGoogle Scholar
• Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet. Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X
   PubMed Web of Science ®Google Scholar
• Couser, G. P. (2008). Challenges and opportunities for preventing depression in the workplace: A review of the evidence supporting workplace factors and interventions. Journal of Occupational and Environmental Medicine, 50(4), 411–427. http://www.jstor.org/stable/44998668 https://doi.org/10.1097/JOM.0b013e318168efe2
   PubMed Web of Science ®Google Scholar
• Cummins, N., Epps, J., Breakspear, M., & Goecke, R. (2011). An investigation of depressed speech detection: Features and normalization. In Proceedings of interspeech (pp. 2997–3000). ISCA.
   Google Scholar
• Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. F. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10–49. https://doi.org/10.1016/j.specom.2015.03.004
   Web of Science ®Google Scholar
• Darcy, A., Beaudette, A., Chiauzzi, E., Daniels, J., Goodwin, K., Mariano, T. Y., Wicks, P., & Robinson, A. (2023). Anatomy of a Woebot® (WB001): Agent guided CBT for women with postpartum depression. Expert Review of Medical Devices, 19(4), 287–301. PMID: 35748029 https://doi.org/10.1080/17434440.2022.2075726
   Web of Science ®Google Scholar
• Dedoncker, J., Vanderhasselt, M. A., Ottaviani, C., & Slavich, G. M. (2021). Mental health during the COVID-19 pandemic and beyond: The importance of the vagus nerve for biopsychosocial resilience. Neuroscience and Biobehavioral Reviews, 125, 1–10. https://doi.org/10.1016/j.neubiorev.2021.02.010
   PubMed Web of Science ®Google Scholar
• DeSouza, D. D., Robin, J., Gumus, M., & Yeung, A. (2021). Natural language processing as an emerging tool to detect late-life depression. Frontiers in Psychiatry, 12, 719125. PMID: 34552519; PMCID: PMC8450440 https://doi.org/10.3389/fpsyt.2021.719125
   PubMed Web of Science ®Google Scholar
• Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., Schatzberg, A. F., Sudheimer, K., Keller, J., Mayberg, H. S., Gunning, F. M., Alexopoulos, G. S., Fox, M. D., Pascual-Leone, A., Voss, H. U., … Liston, C. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23(1), 28–38. https://doi.org/10.1038/nm.4246
   PubMed Web of Science ®Google Scholar
• Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037
   PubMed Web of Science ®Google Scholar
• Engberg, A. (2017). Mental health chatbots—the future of therapy? https://www.himssinsights.eu/mental-health-chatbots-future-therapy
   Google Scholar
• Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
   PubMed Web of Science ®Google Scholar
• Fitzpatrick, K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
   PubMed Web of Science ®Google Scholar
• Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomized controlled trial. JMIR Mental Health, 5(4), e64. PMID: 30545815 https://doi.org/10.2196/mental.9782
   PubMed Web of Science ®Google Scholar
• Gao, S., Calhoun, V. D., & Sui, J. (2018). Machine learning in major depression: From classification to treatment outcome prediction. CNS Neuroscience & Therapeutics, 24(11), 1037–1052. https://doi.org/10.1111/cns.13048
   PubMed Web of Science ®Google Scholar
• Hariman, K., Ventriglio, A., & Bhugra, D. (2019). The future of digital psychiatry. Current Psychiatry Reports, 21(9), 88. https://doi.org/10.1007/s11920-019-1074-4
   PubMed Web of Science ®Google Scholar
• Ho, A., Hancock, J., & Miner, A. (2018). Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. The Journal of Communication, 68(4), 712–733. PMID: 30100620 https://doi.org/10.1093/joc/jqy026
   PubMed Web of Science ®Google Scholar
• Hungerbuehler, I., Daley, K., Cavanagh, K., Garcia Claro, H., & Kapps, M. (2021). Chatbot-based assessment of employees’ mental health: Design process and pilot implementation. JMIR Formative Research, 5(4), e21678. PMID: 33881403; PMCID: PMC8100879 https://doi.org/10.2196/21678
   PubMedGoogle Scholar
• Iyortsuun, N. K., Kim, S. H., Jhon, M., Yang, H. J., & Pant, S. (2023). A review of machine learning and deep learning approaches on mental health diagnosis. Healthcare (Basel, Switzerland), 11(3), 285. PMID: 36766860; PMCID: PMC9914523 https://doi.org/10.3390/healthcare11030285
   PubMedGoogle Scholar
• Jones, S. P., Patel, V., Saxena, S., Radcliffe, N., Ali Al-Marri, S., & Darzi, A. (2014). How Google’s ‘Ten Things We Know To Be True’ could guide the development of mental health mobile apps. Health Affairs (Project Hope), 33(9), 1603–1611. PMID: 25201665 https://doi.org/10.1377/hlthaff.2014.0380
   PubMed Web of Science ®Google Scholar
• Karkosz, S., Szymański, R., Sanna, K., & Michałowski, J. (2024). Effectiveness of a web-based and mobile therapy chatbot on anxiety and depressive symptoms in subclinical young adults: Randomized controlled trial. JMIR Formative Research, 8, e47960. PMID: 38506892; PMCID: PMC10993129 https://doi.org/10.2196/47960
   PubMedGoogle Scholar
• Kaywan, P., Ahmed, K., Ibaida, A., Miao, Y., & Gu, B. (2023). Early detection of depression using a conversational AI bot: A non-clinical trial. PloS One, 18(2), e0279743. https://doi.org/10.1371/journal.pone.0279743
   PubMed Web of Science ®Google Scholar
• Khosla, A., Khandnor, P., & Chand, T. (2022). Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybernetics and Biomedical Engineering, 42(1), 108–142. https://doi.org/10.1016/j.bbe.2021.12.005
   Web of Science ®Google Scholar
• Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4
   PubMed Web of Science ®Google Scholar
• Kim, N. H., Kim, J. M., Park, D. M., Ji, S. R., & Kim, J. W. (2022). Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing. Digital Health, 8: 20552076221114204. 20552076221114204 https://doi.org/10.1177/20552076221114204
   Web of Science ®Google Scholar
• Kok, R. M., & Reynolds, C. F. (2017). Management of depression in older adults: A review. JAMA, 317(20), 2114–2122. https://doi.org/10.1001/jama.2017.5706
   PubMed Web of Science ®Google Scholar
• Koleck, T. A., Dreisbach, C., Bourne, P. E., & Bakken, S. (2019). Natural language processing of symptoms documented in free-text narratives of electronic health records: A systematic review. Journal of the American Medical Informatics Association: JAMIA, 26(4), 364–379. PMID: 30726935; PMCID: PMC6657282 https://doi.org/10.1093/jamia/ocy173
   PubMed Web of Science ®Google Scholar
• Koops, S., Brederoo, S. G., de Boer, J. N., Nadema, F. G., Voppel, A. E., & Sommer, I. E. (2023). Speech as a biomarker for depression. CNS & Neurological Disorders Drug Targets, 22(2), 152–160. PMID: 34961469 https://doi.org/10.2174/1871527320666211213125847
   PubMed Web of Science ®Google Scholar
• Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology: official Publication of the American College of Neuropsychopharmacology, 46(1), 176–190. https://doi.org/10.1038/s41386-020-0767-z
   PubMed Web of Science ®Google Scholar
• Krishnamurti, T., Allen, K., Hayani, L., Rodriguez, S., & Davis, A. L. (2022). Identification of maternal depression risk from natural language collected in a mobile health app. Procedia Computer Science, 206, 132–140. https://doi.org/10.1016/j.procs.2022.09.092
   PubMedGoogle Scholar
• Lang, H., Mingyue, N., Prayag, T., Pekka, M., Rui, S., Jiewei, J., Chenguang, G., Hongyu, W., Songtao, D., Zhongmin, W., Xiaoying, P., & Wei, D. (2022). Deep learning for depression recognition with audiovisual cues: A review. Information Fusion, 80, 56–86. https://doi.org/10.1016/j.inffus.2021.10.012
   Web of Science ®Google Scholar
• Lee, S., Suh, S. W., Kim, T., Kim, K., Lee, K. H., Lee, J. R., Han, G., Hong, J. W., Han, J. W., Lee, K., & Kim, K. W. (2021). Screening major depressive disorder using vocal acoustic features in the elderly by sex. Journal of Affective Disorders, 291, 15–23. https://doi.org/10.1016/j.jad.2021.04.098
   PubMed Web of Science ®Google Scholar
• Lim, S. M., Shiau, C. W. C., Cheng, L. J., & Lau, Y. (2022). Chatbot-delivered psychotherapy for adults with depressive and anxiety symptoms: A systematic review and meta-regression. Behavior Therapy, 53(2), 334–347. https://doi.org/10.1016/j.beth.2021.09.007
   PubMed Web of Science ®Google Scholar
• Lin, E., Kuo, P.-H., Liu, Y.-L., Yu, Y. W.-Y., Yang, A. C., & Tsai, S.-J. (2020). Prediction of antidepressant treatment response and remission using an ensemble machine learning framework. Pharmaceuticals (Basel, Switzerland), 13(10), 305. https://doi.org/10.3390/ph13100305
   PubMedGoogle Scholar
• Lin, E., Kuo, P. H., Liu, Y. L., Yu, Y. W., Yang, A. C., & Tsai, S. J. (2018). A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Frontiers in Psychiatry, 9, 290. https://doi.org/10.3389/fpsyt.2018.00290
   PubMed Web of Science ®Google Scholar
• Lin, E., Lin, C. H., & Lane, H. Y. (2020). Precision psychiatry applications with pharmacogenomics: Artificial intelligence and machine learning approaches. International Journal of Molecular Sciences, 21(3), 969. https://doi.org/10.3390/ijms21030969
   PubMed Web of Science ®Google Scholar
• Lin, E., Lin, C. H., & Lane, H. Y. (2021). Machine learning and deep learning for the pharmacogenomics of antidepressant treatments. Clinical Psychopharmacology and Neuroscience: The Official Scientific Journal of the Korean College of Neuropsychopharmacology, 19(4), 577–588. https://doi.org/10.9758/cpn.2021.19.4.577
   PubMed Web of Science ®Google Scholar
• Lin, J., Paganini, S., Sander, L., Lüking, M., Ebert, D. D., Buhrman, M., Andersson, G., & Baumeister, H. (2017). An internet-based intervention for chronic pain: A three-arm randomized controlled study of the effectiveness of guided and unguided acceptance and commitment therapy. Deutsches Arzteblatt International, 114(41), 681–688. https://doi.org/10.3238/arztebl.2017.0681
   PubMedGoogle Scholar
• Low, D. M., Bentley, K. H., & Ghosh, S. S. (2020). Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology, 5(1), 96–116. https://doi.org/10.1002/lio2.354
   PubMed Web of Science ®Google Scholar
• Low, L. S. A., Maddage, C. J., Lech, M., Sheeber, L. B., & Allen, N. B. (2011). Detection of clinical depression in adolescents; speech during family interactions. IEEE Transactions on Bio-Medical Engineering, 58(3), 574–586. https://doi.org/10.1109/TBME.2010.2091640
   PubMed Web of Science ®Google Scholar
• Lucas, G. M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L. P. (2017). Reporting mental health symptoms: Breaking down barriers to care with virtual human interviewers. Frontiers in Robotics and AI, 4, 51. https://doi.org/10.3389/frobt.2017.00051
   Google Scholar
• Maciukiewicz, M., Marshe, V. S., Hauschild, A. C., Foster, J. A., Rotzinger, S., Kennedy, J. L., Kennedy, S. H., Müller, D. J., & Geraci, J. (2018). GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. Journal of Psychiatric Research, 99, 62–68. https://doi.org/10.1016/j.jpsychires.2017.12.009
   PubMed Web of Science ®Google Scholar
• Malgaroli, M., Hull, T. D., Zech, J. M., & Althoff, T. (2023). Natural language processing for mental health interventions: A systematic review and research framework. Translational Psychiatry, 13(1), 309. https://doi.org/10.1038/s41398-023-02592-2
   PubMed Web of Science ®Google Scholar
• Mehltretter, J., Rollins, C., Benrimoh, D., Fratila, R., Perlman, K., Israel, S., Miresco, M., Wakid, M., & Turecki, G. (2019). Analysis of features selected by a deep learning model for differential treatment selection in depression. Frontiers in Artificial Intelligence, 2, 31. https://doi.org/10.3389/frai.2019.00031
   PubMedGoogle Scholar
• Moore, E., Clements, M. A., Peifer, J. W., & Weisser, L. (2008). Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Transactions on Bio-Medical Engineering, 55(1), 96–107. https://doi.org/10.1109/TBME.2007.903506
   PubMed Web of Science ®Google Scholar
• Mundt, J. C., Snyder, P. J., Cannizzaro, M. S., Chappie, K., & Geralts, D. S. (2007). Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. Journal of Neurolinguistics, 20(1), 50–64. https://doi.org/10.1016/j.jneuroling.2006.04.001
   PubMed Web of Science ®Google Scholar
• Nardodkar, R., Pathare, S., Ventriglio, A., Castaldelli-Maia, J., Javate, K. R., Torales, J., & Bhugra, D. (2016). Legal protection of the right to work and employment for persons with mental health problems: A review of legislation across the world. International Review of Psychiatry (Abingdon, England), 28(4), 375–384. https://doi.org/10.1080/09540261.2016.1210575
   PubMed Web of Science ®Google Scholar
• Nielsen, A. N., Barch, D. M., Petersen, S. E., Schlaggar, B. L., & Greene, D. J. (2020). Machine learning with neuroimaging: Evaluating its applications in psychiatry. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 5(8), 791–798. https://doi.org/10.1016/j.bpsc.2019.11.007
   PubMedGoogle Scholar
• Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 28. https://doi.org/10.17705/1CAIS.02428
   Google Scholar
• Otero-González, I., Pacheco-Lorenzo, M. R., Fernández-Iglesias, M. J., & Anido-Rifón, L. E. (2024). Conversational agents for depression screening: A systematic review. International Journal of Medical Informatics, 181, 105272. https://doi.org/10.1016/j.ijmedinf.2023.105272
   PubMed Web of Science ®Google Scholar
• Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F., 3rd., & Aizenstein, H. J. (2015). Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. International Journal of Geriatric Psychiatry, 30(10), 1056–1067. https://doi.org/10.1002/gps.4262
   PubMed Web of Science ®Google Scholar
• Philip, P., Micoulaud-Franchi, J. A., Sagaspe, P., Sevin, E., Olive, J., Bioulac, S., & Sauteraud, A. (2017). Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Scientific Reports, 7(1), 42656. https://doi.org/10.1038/srep42656
   PubMed Web of Science ®Google Scholar
• Prochaska, J. J., Vogel, E. A., Chieng, A., Kendra, M., Baiocchi, M., Pajarito, S., & Robinson, A. (2021). A therapeutic relational agent for reducing problematic substance use (Woebot): Development and usability study. Journal of Medical Internet Research, 23(3), e24850. https://doi.org/10.2196/24850
   PubMed Web of Science ®Google Scholar
• Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Bürger, C., Münker, S., Mühlmann, L., Wahl, P., Heindel, W., Arolt, V., Alferink, J., Zwanzger, P., Zavorotnyy, M., Kugel, H., & Dannlowski, U. (2016). Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry, 73(6), 557–564. https://doi.org/10.1001/jamapsychiatry.2016.0316
   PubMed Web of Science ®Google Scholar
• Rema, J., Novais, F., & Telles-Correia, D. (2022). Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Current Topics in Medicinal Chemistry, 22(15), 1261–1269. https://doi.org/10.2174/1568026621666211004095917
   PubMed Web of Science ®Google Scholar
• Richards, B., Tsao, D., & Zador, A. (2022). The application of artificial intelligence to biology and neuroscience. Cell, 185(15), 2640–2643. https://doi.org/10.1016/j.cell.2022.06.047
   PubMed Web of Science ®Google Scholar
• Rodda, J., Walker, Z., & Carter, J. (2011). Depression in older adults. BMJ (Clinical Research ed.), 343(sep28 1), d5219–d5219. https://doi.org/10.1136/bmj.d5219
   PubMedGoogle Scholar
• Rutledge, R. B., Chekroud, A. M., & Huys, Q. J. (2019). Machine learning and big data in psychiatry: Toward clinical applications. Current Opinion in Neurobiology, 55, 152–159. https://doi.org/10.1016/j.conb.2019.02.006
   PubMed Web of Science ®Google Scholar
• Safayari, A., & Bolhasani, H. (2021). Depression diagnosis by deep learning using EEG signals: A systematic review. Medicine in Novel Technology and Devices, 12, 100102. https://doi.org/10.1016/j.medntd.2021.100102
   Google Scholar
• Sameera, A., & John, D. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7) https://doi.org/10.14569/IJACSA.2015.060712
   Google Scholar
• Santomauro, D. F, COVID-19 Mental Disorders Collaborators. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet (London, England), 398(10312), 1700–1712. https://doi.org/10.1016/S0140-6736(21)02143-7
   PubMed Web of Science ®Google Scholar
• Sato, J. R., Moll, J., Green, S., Deakin, J. F., Thomaz, C. E., & Zahn, R. (2015). Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Research, 233(2), 289–291. https://doi.org/10.1016/j.pscychresns.2015.07.001
   PubMed Web of Science ®Google Scholar
• Scherer, S., Pestian, J., & Morency, L. P. (2013b). Investigating the speech characteristics of suicidal adolescents [Paper presentation]. Proceedings of ICASSP (pp. 709–713). IEEE.
   Google Scholar
• Scherer, S., Stratou, G., Gratch, J., & Morency, L. (2013a). Investigating voice quality as a speaker-independent indicator of depression and PTSD. In Proceedings of interspeech (pp. 847–851). ISCA.
   Google Scholar
• Schnyder, N., Panczak, R., Groth, N., & Schultze-Lutter, F. (2017). Association between mental health-related stigma and active help-seeking: Systematic review and meta-analysis. The British Journal of Psychiatry: The Journal of Mental Science, 210(4), 261–268. https://doi.org/10.1192/bjp.bp.116.189464
   PubMed Web of Science ®Google Scholar
• Scholten, M. R., Kelders, S. M., & Van Gemert-Pijnen, J. E. (2017). Self-guided web-based interventions: Scoping review on user needs and the potential of embodied conversational agents to address them. Journal of Medical Internet Research, 19(11), e383. https://doi.org/10.2196/jmir.7351
   PubMed Web of Science ®Google Scholar
• Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151
   PubMed Web of Science ®Google Scholar
• Silverman, S. E., & Silverman, M. K. (2006). Methods and apparatus for evaluating near-term suicidal risk using vocal parameters.
   Google Scholar
• Smith, M., Dietrich, B. J., Bai, E., & Bockholt, H. J. (2019). Vocal pattern detection of depression among older adults. International Journal of Mental Health Nursing, 29(3), 440–449. https://doi.org/10.1111/inm.12678
   PubMed Web of Science ®Google Scholar
• Squarcina, L., Villa, F. M., Nobile, M., Grisan, E., & Brambilla, P. (2021). Deep learning for the prediction of treatment response in depression. Journal of Affective Disorders, 281, 618–622. https://doi.org/10.1016/j.jad.2020.11.104
   PubMed Web of Science ®Google Scholar
• Tai, A. M. Y., Albuquerque, A., Carmona, N. E., Subramanieapillai, M., Cha, D. S., Sheko, M., Lee, Y., Mansur, R., & McIntyre, R. S. (2019). Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artificial Intelligence in Medicine, 99, 101704. https://doi.org/10.1016/j.artmed.2019.101704
   PubMed Web of Science ®Google Scholar
• Tandon, N., & Tandon, R. (2019). Machine learning in psychiatry - standards and guidelines. Asian Journal of Psychiatry, 44, A1–A4. https://doi.org/10.1016/j.ajp.2019.09.009
   PubMed Web of Science ®Google Scholar
• Trevino, A., Quatieri, T., & Malyska, N. (2011). Phonologically-based biomarkers for major depressive disorder. EURASIP Journal on Advances in Signal Processing, 2011(1), 1–18. https://doi.org/10.1186/1687-6180-2011-42
   Google Scholar
• Tung, C., & Lu, W. (2016). Analyzing depression tendency of web posts using an event-driven depression tendency warning model. Artificial Intelligence in Medicine, 66, 53–62. https://doi.org/10.1016/j.artmed.2015.10.003
   PubMed Web of Science ®Google Scholar
• Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. Canadian Journal of Psychiatry, 64(7), 456–464.
   Google Scholar
• Volpe, U., Ventriglio, A., Bellomo, A., Kadhum, M., Lewis, T., Molodynski, A., Sampogna, G., & Fiorillo, A. (2019). Mental health and wellbeing among Italian medical students: a descriptive study. International Review of Psychiatry (Abingdon, England), 31(7-8), 569–573. https://doi.org/10.1080/09540261.2019.1654718
   PubMed Web of Science ®Google Scholar
• Williamson, J. R., Quatieri, T. F., Helfer, B. S., Horwitz, R., Yu, B., & Mehta, D. D. (2013). Vocal biomarkers of depression based on motor incoordination. In T. F. Quatieri, B. Schuller, S. Narayanan, A. Batliner, S. Steidl, & J. Schwenker (Eds.), Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, Barcelona, Spain (pp. 41–48). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/2512530.2512531
   Google Scholar
• Winter, N. R., & Hahn, T. (2020). Big Data, KI und Maschinenlernen auf dem Weg zur Precision-Psychiatry – wie verändern sie den therapeutischen Alltag? [Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?]. Fortschritte Der Neurologie-Psychiatrie, 88(12), 786–793. https://doi.org/10.1055/a-1234-6247
   PubMed Web of Science ®Google Scholar
• Yan, W. J., Ruan, Q. N., & Jiang, K. (2022). Challenges for Artificial Intelligence in Recognizing Mental Disorders. Diagnostics (Basel, Switzerland), 13(1), 2. https://doi.org/10.3390/diagnostics13010002
   PubMedGoogle Scholar