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Akhavan Abiri, F., & Shairi, M. R. (2020a). Short Forms of Symptom Checklist (SCL): Investigation of validity & Reliability. Clinical Psychology and Personality, 18(1), 137–162. https://doi.org/10.22070/CPAP.2020.2929 Akhavan Abiri, F., & Shairi, M. R. (2020b). Validity and Reliability of Symptom Checklist-90-Revised (SCL-90-R) and Brief Symptom Inventory-53 (BSI-53). Clinical Psychology and Personality, 17(2), 169–195. https://doi.org/10.22070/CPAP.2020.2916 Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387. https://doi.org/10.1016/J.IJINFOMGT.2021.102387 Americans, N., Article, S., Haghir, H., Mokhber, N., Azarpazhooh, M. R., Haghighi, M. B. … Plan, Y. (2013). … World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders. IACAPAP E-Textbook of Child and Adolescent Mental Health, 55(1993), 135–139. https://doi.org/10.4103/0019 Balcombe, L., & De Leo, D. (2021). Digital Mental Health Challenges and the Horizon Ahead for Solutions XSL • FO RenderX. JMIR Ment Health, 8(3), 1. https://doi.org/10.2196/26811 Barker-Collo, S. L. (2003). Culture and validity of the Symptom Checklist-90-Revised and Profile of Mood States in a New Zealand student sample. Cultural Diversity and Ethnic Minority Psychology, 9(2), 185–196. https://doi.org/10.1037/1099-9809.9.2.185 Bernet, W., Baker, A. J. L., & Verrocchio, M. C. (2015). Symptom Checklist-90-Revised Scores in Adult Children Exposed to Alienating Behaviors: An Italian Sample. Journal of Forensic Sciences, 60(2), 357–362. https://doi.org/10.1111/1556-4029.12681 Borgesius, F. J. Z. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. 24(10), 1572–1593. https://doi.org/10.1080/13642987.2020.1743976 Buhmann, A., & Fieseler, C. (2021). Towards a deliberative framework for responsible innovation in artificial intelligence. Technology in Society, 64, 101475. https://doi.org/10.1016/J.TECHSOC.2020.101475 Casado-Lumbreras, C., Rodríguez-González, A., Álvarez-Rodríguez, J. M., & Colomo-Palacios, R. (2012). PsyDis: Towards a diagnosis support system for psychological disorders. Expert Systems with Applications, 39(13), 11391–11403. https://doi.org/10.1016/j.eswa.2012.04.033 Chandler, C., Foltz, P. W., & Elvevåg, B. (2020). Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness. Schizophrenia Bulletin, 46(1), 11–14. https://doi.org/10.1093/SCHBUL/SBZ105 Chekroud, A. M., Gueorguieva, R., Krumholz, H. M., Trivedi, M. H., Krystal, J. H., & McCarthy, G. (2017). Reevaluating the efficacy and predictability of antidepressant treatments: A symptom clustering approach. JAMA Psychiatry, 74(4), 370–378. https://doi.org/10.1001/jamapsychiatry.2017.0025 Chen, I. H., Lin, C. Y., Zheng, X., & Griffiths, M. D. (2020). Assessing Mental Health for China’s Police: Psychometric Features of the Self-Rating Depression Scale and Symptom Checklist 90-Revised. International Journal of Environmental Research and Public Health 2020, 17(8), 2737. https://doi.org/10.3390/IJERPH17082737.17 Chen, I. Y., Szolovits, P., & Ghassemi, M. (2019). Can AI help reduce disparities in general medical and mental health care? AMA Journal of Ethics, 21(2), 167–179. https://doi.org/10.1001/AMAJETHICS.2019.167 Chen, S., Stromer, D., Alabdalrahim, H. A., Schwab, S., Weih, M., & Maier, A. (2020). Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Scientific Reports, 10(1), 1–11. https://doi.org/10.1038/s41598-020-74710-9 Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature 2016, 538:7625(7625), 311–313. https://doi.org/10.1038/538311a. 538 D’Alfonso, S. (2020). AI in mental health. Current Opinion in Psychology, 36, 112–117. https://doi.org/10.1016/J.COPSYC.2020.04.005 D’Aquin, M., Troullinou, P., O, N. E., Cullen, A., Faller, G., & Holden, L. (2018). Towards an “Ethics by Design’’’ Methodology for AI Research Projects.” 18. https://doi.org/10.1145/3278721 Dennehy, D., Pappas, I. O., Wamba, S. F., & Michael, K. (2021). Socially responsible information systems development: the role of AI and business analytics. Information Technology and People, 34(6), 1541–1550. https://doi.org/10.1108/ITP-10-2021-871 Derogatis, L. (2017). Symptom Checklist-90-Revised, Brief Symptom Inventory, and BSI-18. - PsycNET.Handbook of Psychological Assessment in Primary Care Settings,599–629. https://psycnet.apa.org/record/2017-23747-023 Derogatis, L., & Fitzpatrick, M. (2004). The SCL-90-R, the Brief Symptom Inventory (BSI), and the BSI-18. In The use of psychological testing for treatment planning and outcomes assessment: Instruments for adults (pp. 1–41). https://psycnet.apa.org/record/2004-14941-001 Derogatis, L., & Spencer, P. (1993). Brief Symptom Inventory Dignum, V., Baldoni, M., Baroglio, C., Caon, M., Chatila, R., Dennis, L. … De Wildt, T. (2018). Ethics by Design: Necessity or Curse? AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 18, 60–66. https://doi.org/10.1145/3278721.3278745 Docrat, S., Besada, D., Cleary, S., Daviaud, E., & Lund, C. (2019). Mental health system costs, resources and constraints in South Africa: a national survey. Health Policy and Planning, 34(9), 706–719. https://doi.org/10.1093/HEAPOL/CZZ085 Fabiano, F., & Haslam, N. (2020). Diagnostic inflation in the DSM: A meta-analysis of changes in the stringency of psychiatric diagnosis from DSM-III to DSM-5. Clinical Psychology Review, 80, 101889. https://doi.org/10.1016/J.CPR.2020.101889 Fellmeth, G., Harrison, S., Opondo, C., Nair, M., Kurinczuk, J. J., & Alderdice, F. (2021). Validated screening tools to identify common mental disorders in perinatal and postpartum women in India: a systematic review and meta-analysis. BMC Psychiatry 2021, 21:1(1), 1–10. https://doi.org/10.1186/S12888-021-03190-6. 21 Floridi, L., & Cowls, J. (2021). A Unified Framework of Five Principles for AI in Society. Philosophical Studies Series, 144, 5–17. https://doi.org/10.1007/978-3-030-81907-1_2 Fosso Wamba, S., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2021). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change, 164, 120482. https://doi.org/10.1016/J.TECHFORE.2020.120482 Fosso Wamba, S., & Queiroz, M. M. (2021). Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions. Information Systems Frontiers, 1–16. https://doi.org/10.1007/S10796-021-10142-8/TABLES/7 Galesic, M., & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73(2), 349–360. https://doi.org/10.1093/poq/nfp031 Gallardo-Pujol, D., & Pereda, N. (2013). Person-environment transactions: persionality traits moderate and mediate the effects. Personality and Mental Health, 7(April 2012), 102–113. https://doi.org/10.1002/pmh Garcia-Zattera, M. J., Mutsvari, T., Jara, A., Declerck, D., & Lesaffre, E. (2010). Correcting for misclassification for a monotone disease process with an application in dental research. Statistics in Medicine, 29(30), 3103–3117. https://doi.org/10.1002/sim.3906 Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5 Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 2019, 21:11(11), 1–18. https://doi.org/10.1007/S11920-019-1094-0. 21 Gupta, M., Parra, C. M., & Dennehy, D. (2021). Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter? Information Systems Frontiers, 1–17. https://doi.org/10.1007/S10796-021-10156-2/TABLES/5 Hanna, F., Barbui, C., Dua, T., Lora, A., van Altena, M. R., & Saxena, S. (2018). Global mental health: how are we doing? World Psychiatry, 17(3), 368. https://doi.org/10.1002/WPS.20572 Hao, B., Li, L., Li, A., & Zhu, T. (2013). Predicting mental health status on social media a preliminary study on microblog. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8024 LNCS(PART 2), 101–110. https://doi.org/10.1007/978-3-642-39137-8-12 Hardt, J., & Gerbershagen, H. U. (2001). Cross-validation of the SCL-27: A short psychometric screening instrument for chronic pain patients. European Journal of Pain, 5(2), 187–197. https://doi.org/10.1053/eujp.2001.0231 Hardt, J. (2008). The symptom checklist-27-plus (SCL-27-plus): a modern conceptualization of a traditional screening instrument. Psycho-Social Medicine, 5, Doc08 Hastie, T., Tibshirani, R., & Friedman, J. (2009). Support Vector Machines and Flexible Discriminants. In The elements of statistical learning (pp. 1–42). https://doi.org/10.1007/b94608_12 Hastie, T., Tibshirani, R., & Friedman, J. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer Series in Statistics Hildenbrand, A. K., Nicholls, E. G., Aggarwal, R., Brody-Bizar, E., & Daly, B. P. (2015). Symptom Checklist-90-Revised (SCL-90-R). The Encyclopedia of Clinical Psychology, 1–5. https://doi.org/10.1002/9781118625392.wbecp495 Holi, M. (2003). Assessment of psychiatric symptoms using the SCL-90. [Matti Holi] IBM (2020). Trustworthy AI. https://www.ibm.com/watson/trustworthy-ai Imperatori, C., Bianciardi, E., Niolu, C., Fabbricatore, M., Gentileschi, P., Lorenzo, G. … Innamorati, M. (2020). The Symptom-Checklist-K-9 (SCL-K-9) Discriminates between Overweight/Obese Patients with and without Significant Binge Eating Pathology: Psychometric Properties of an Italian Version. Nutrients 2020, 12(3), 674. https://doi.org/10.3390/NU12030674. 12 Iphofen, R., & Kritikos, M. (2019). Regulating artificial intelligence and robotics: ethics by design in a digital society. https://doi.org/10.1080/21582041.201816 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 103). Springer New York. https://doi.org/10.1007/978-1-4614-7138-7 Jenkins, R., Kydd, R., Mullen, P., Thomson, K., Sculley, J., Kuper, S. … Wong, M. L. (2010). International migration of doctors, and its impact on availability of psychiatrists in low and middle income countries. PLoS ONE, 5(2), 1–9. https://doi.org/10.1371/journal.pone.0009049 Johnson, M., Albizri, A., & Harfouche, A. (2021). Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing. Information Systems Frontiers, 1–17. https://doi.org/10.1007/s10796-021-10137-5 Johnson, M., Albizri, A., Harfouche, A., & Tutun, S. (2021). Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-04-2021-0248/FULL/XML Johnson, M., Albizri, A., & Simsek, S. (2020). Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Annals of Operations Research, 1–31. https://doi.org/10.1007/s10479-020-03872-6 Khan, M. E., & Tutun, S. (2021). Understanding and Predicting Organ Donation Outcomes Using Network-based Predictive Analytics. Procedia Computer Science, 185, 185–192. https://doi.org/10.1016/J.PROCS.2021.05.020 Khanal, P., Devkota, N., Dahal, M., Paudel, K., & Joshi, D. (2020). Mental health impacts among health workers during COVID-19 in a low resource setting: a cross-sectional survey from Nepal. Globalization and Health, 2020 16:1(1), 1–12. https://doi.org/10.1186/S12992-020-00621-Z. 16 Kilbourne, A. M., Beck, K., Spaeth-Rublee, B., Ramanuj, P., O’Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018). Measuring and improving the quality of mental health care: a global perspective. World Psychiatry, 17(1), 30–38. https://doi.org/10.1002/WPS.20482 Kim, J. H., & Jang, S. (2016). The Relationship Between Job Stress, Job Satisfaction, and the Symptom Checklist-90-Revision (SCL-90-R) in Marine Officers on Board. Journal of Preventive Medicine and Public Health, 49(6), 376. https://doi.org/10.3961/JPMPH.16.046 Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A. … Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences of the United States of America, 114(13), 3521–3526. https://doi.org/10.1073/PNAS.1611835114/-/DCSUPPLEMENTAL Kruyen, P. M., Emons, W. H. M., & Sijtsma, K. (2013). On the Shortcomings of Shortened Tests: A Literature Review. International Journal of Testing, 13(3), 223–248. https://doi.org/10.1080/15305058.2012.703734 Li, P., Wang, F., Ji, G. Z., Miao, L., You, S., & Chen, X. (2018). The psychological results of 438 patients with persisting GERD symptoms by Symptom Checklist 90-Revised (SCL-90-R) questionnaire. Medicine (United States), 97(5), 10768. https://doi.org/10.1097/MD.0000000000009783 Lin, L., Hu, P. J. H., & Liu Sheng, O. R. (2006). A decision support system for lower back pain diagnosis: Uncertainty management and clinical evaluations. Decision Support Systems, 42(2), 1152–1169. https://doi.org/10.1016/j.dss.2005.10.007 Lundqvist, L. O., & Schröder, A. (2021). Evaluation of the SCL-9S, a short version of the symptom checklist-90-R, on psychiatric patients in Sweden by using Rasch analysis. https://doi.org/10.1080/08039488.2021.1901988 Luxton, D. D. (2016). An Introduction to Artificial Intelligence in Behavioral and Mental Health Care. Artificial Intelligence in Behavioral and Mental Health Care, 1–26. https://doi.org/10.1016/B978-0-12-420248-1.00001-5 Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), https://doi.org/10.1177/2053951716679679 Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. The Ethics of Biomedical Big Data (1st ed., pp. 445–480). Cham: Springer. https://doi.org/10.1007/978-3-319-33525-4_19 Morley, J., Machado, C., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2019). The Debate on the Ethics of AI in Health Care: A Reconstruction and Critical Review. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3486518 Mueller, A., Candrian, G., Grane, V. A., Kropotov, J. D., Ponomarev, V. A., & Baschera, G. M. (2011). Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: A validation study. Nonlinear Biomedical Physics, 5(1), 1–18. https://doi.org/10.1186/1753-4631-5-5 Nie, D., Ning, Y., & Zhu, T. (2012). Predicting mental health status in the context of web browsing. Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012, 185–189. https://doi.org/10.1109/WI-IAT.2012.196 Ogasawara, K., Nakamura, Y., Kimura, H., Aleksic, B., & Ozaki, N. (2017). Issues on the diagnosis and etiopathogenesis of mood disorders: reconsidering DSM-5. Journal of Neural Transmission, 2017 125:2(2), 211–222. https://doi.org/10.1007/S00702-017-1828-2. 125 Olsen, L. R., Mortensen, E. L., & Bech, P. (2004). Prevalence of major depression and stress indicators in the Danish general population. Acta Psychiatrica Scandinavica, 109(2), 96–103. https://doi.org/10.1046/j.0001-690X.2003.00231.x Park, S. C., & Kim, Y. K. (2020). Anxiety Disorders in the DSM-5: Changes, Controversies, and Future Directions. Advances in Experimental Medicine and Biology, 1191, 187–196. https://doi.org/10.1007/978-981-32-9705-0_12 Parra, C. M., Gupta, M., & Mikalef, P. (2021). Information and communication technologies (ICT)-enabled severe moral communities and how the (Covid19) pandemic might bring new ones. International Journal of Information Management, 57, 102271. https://doi.org/10.1016/J.IJINFOMGT.2020.102271 Pechenizkiy, M., Tsymbal, A., Puuronen, S., & Pechenizkiy, O. (2006). Class noise and supervised learning in medical domains: The effect of feature extraction. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2006, 708–713. https://doi.org/10.1109/CBMS.2006.65 Perkins, A., Ridler, J., Browes, D., Peryer, G., Notley, C., & Hackmann, C. (2018). Experiencing mental health diagnosis: a systematic review of service user, clinician, and carer perspectives across clinical settings. The Lancet Psychiatry, 5(9), 747–764. https://doi.org/10.1016/S2215-0366(18)30095-6 Petersen, I., Bhana, A., Fairall, L. R., Selohilwe, O., Kathree, T., Baron, E. C. … Lund, C. (2019). Evaluation of a collaborative care model for integrated primary care of common mental disorders comorbid with chronic conditions in South Africa. BMC Psychiatry 2019, 19:1(1), 1–11. https://doi.org/10.1186/S12888-019-2081-Z. 19 Price, W. N. I. (2019). Medical AI and Contextual Bias. Harvard Journal of Law & Technology (Harvard JOLT), 33. https://heinonline.org/HOL/Page?handle=hein.journals/hjlt33&id=71&div=&collection= Prinz, U., Nutzinger, D. O., Schulz, H., Petermann, F., Braukhaus, C., & Andreas, S. (2013). Comparative psychometric analyses of the SCL-90-R and its short versions in patients with affective disorders. BMC Psychiatry, 13, 1–9. https://doi.org/10.1186/1471-244X-13-104 Ransing, R., Ramalho, R., Orsolini, L., Adiukwu, F., Gonzalez-Diaz, J. M., Larnaout, A. … Kilic, O. (2020). Can COVID-19 related mental health issues be measured? Brain, Behavior, and Immunity, 88, 32. https://doi.org/10.1016/J.BBI.2020.05.049 Razzouk, D., Mari, J. J., Shirakawa, I., Wainer, J., & Sigulem, D. (2006). Decision support system for the diagnosis of schizophrenia disorders. Brazilian Journal of Medical and Biological Research, 39(1), 119–128 Ritchie, H. (2018). Global burden of disease studies: Implications for mental and substance use disorders. In Health Affairs. Issue 6). Project HOPE, 35, https://doi.org/10.1377/HLTHAFF.2016.0082 Rovini, E., Maremmani, C., Moschetti, A., Esposito, D., & Cavallo, F. (2018). Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches. Annals of Biomedical Engineering, 46(12), 2057–2068. https://doi.org/10.1007/s10439-018-2104-9 Rytilä-Manninen, M., Fröjd, S., Haravuori, H., Lindberg, N., Marttunen, M., Kettunen, K., & Therman, S. (2016). Psychometric properties of the Symptom Checklist-90 in adolescent psychiatric inpatients and age- and gender-matched community youth. Child and Adolescent Psychiatry and Mental Health 2016 10:1, 10(1), 1–12. https://doi.org/10.1186/S13034-016-0111-X Schmitz, N., Hartkamp, N., Kiuse, J., Franke, G. H., Reister, G., & Tress, W. (2000). The Symptom Check-List-90-R (SCL-90-R): A German validation study. Quality of Life Research, 9(2), 185–193. https://doi.org/10.1023/A:1008931926181 Schönberger, D. (2019). Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. International Journal of Law and Information Technology, 27(2), 171–203. https://doi.org/10.1093/IJLIT/EAZ004 Sereda, Y., & Dembitskyi, S. (2016). Validity assessment of the symptom checklist SCL-90-R and shortened versions for the general population in Ukraine. BMC Psychiatry 2016 16:1, 16(1), 1–11. https://doi.org/10.1186/S12888-016-1014-3 Simsek, S., Albizri, A., Johnson, M., Custis, T., & Weikert, S. (2020). Predictive data analytics for contract renewals: a decision support tool for managerial decision-making. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-12-2019-0375 Sinyor, M., Schaffer, A., & Levitt, A. (2010). The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: A review. Canadian Journal of Psychiatry, 55(3), 126–135. https://doi.org/10.1177/070674371005500303 Sleep, C., Lynam, D. R., & Miller, J. D. (2021). Personality impairment in the DSM-5 and ICD-11: Current standing and limitations. Current Opinion in Psychiatry, 34(1), 39–43. https://doi.org/10.1097/YCO.0000000000000657 Stewart, S. L., Celebre, A., Hirdes, J. P., & Poss, J. W. (2020). Risk of Suicide and Self-harm in Kids: The Development of an Algorithm to Identify High-Risk Individuals Within the Children’s Mental Health System. Child Psychiatry and Human Development, 51(6), 913. https://doi.org/10.1007/S10578-020-00968-9 Thieme Anja, B., & Doherty Gavin. (2020). Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction (TOCHI), 27(5), 34. https://doi.org/10.1145/3398069 Trivedi, M. H., Kern, J. K., Grannemann, B. D., Altshuler, K. Z., & Sunderajan, P. (2004). A computerized clinical decision support system as a means of implementing depression guidelines. Psychiatric Services, 55(8), 879–885. https://doi.org/10.1176/appi.ps.55.8.879 Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: key problems and solutions. AI & SOCIETY 2021, 1, 1–16. https://doi.org/10.1007/S00146-021-01154-8 Tutun, S., Khasawneh, M. T., & Zhuang, J. (2017). New framework that uses patterns and relations to understand terrorist behaviors. Expert Systems with Applications, 78, 358–375. https://doi.org/10.1016/J.ESWA.2017.02.029 Tutun, S., Tosyali, A., Sangrody, H., Khasawneh, M., Johnson, M., Albizri, A., & Harfouche, A. (2022). Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system. Https://Doi.Org, 2046514, 1–18. https://doi.org/10.1080/2573234X.2022.2046514 Urbán, R., Arrindell, W. A., Demetrovics, Z., Unoka, Z., & Timman, R. (2016). Cross-cultural confirmation of bi-factor models of a symptom distress measure: Symptom Checklist-90-Revised in clinical samples. Psychiatry Research, 239, 265–274. https://doi.org/10.1016/J.PSYCHRES.2016.03.039 Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A. … Arbuckle, M. R. (2017). Challenges and Opportunities in Global Mental Health: a Research-to-Practice Perspective. Current Psychiatry Reports, 19(5), 28. https://doi.org/10.1007/S11920-017-0780-Z Wang, W. M., & Cheung, C. F. (2011). A narrative-based reasoning with applications in decision support for social service organizations. Expert Systems with Applications, 38(4), 3336–3345. https://doi.org/10.1016/j.eswa.2010.08.118 WHO (2018). Mental health: strengthening our response. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response WHO (2020). World Mental Health Day: an opportunity to kick-start a massive scale-up in investment in mental health. https://www.who.int/news/item/27-08-2020-world-mental-health-day-an-opportunity-to-kick-start-a-massive-scale-up-in-investment-in-mental-health Zhang, Z., Lin, W., Liu, M., & Mahmoud, M. (2020). Multimodal Deep Learning Framework for Mental Disorder Recognition. Proceedings – 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020, 344–350. https://doi.org/10.1109/FG47880.2020.00033