<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Consortium PSYCHIATRICUM</journal-id><journal-title-group><journal-title xml:lang="en">Consortium PSYCHIATRICUM</journal-title><trans-title-group xml:lang="ru"><trans-title>Consortium PSYCHIATRICUM</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2712-7672</issn><issn publication-format="electronic">2713-2919</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">11244</article-id><article-id pub-id-type="doi">10.17816/CP11244</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>REVIEW</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>ОБЗОР</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Current status, challenges and future prospects in computational psychiatry: a narrative review</article-title><trans-title-group xml:lang="ru"><trans-title>Современное положение, вызовы и перспективы развития вычислительной психиатрии: нарративный обзор</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9910-2079</contrib-id><contrib-id contrib-id-type="spin">4549-1790</contrib-id><name-alternatives><name xml:lang="en"><surname>Vasilchenko</surname><given-names>Kirill F.</given-names></name><name xml:lang="ru"><surname>Васильченко</surname><given-names>Кирилл Федорович</given-names></name></name-alternatives><address><country country="IL">Israel</country></address><bio xml:lang="en"><p>Cand. Sci (Med.), The Human artificial control Keren (HacK) lab, Azrieli Faculty of Medicine, Bar-Ilan University</p></bio><bio xml:lang="ru"><p>к.м.н., лаборатория вычислительной психиатрии, медицинский факультет Азриэли, Университет Бар-Илан</p></bio><email>kirill.f.vasilchenko@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0429-8460</contrib-id><name-alternatives><name xml:lang="en"><surname>Chumakov</surname><given-names>Egor M.</given-names></name><name xml:lang="ru"><surname>Чумаков</surname><given-names>Егор Максимович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Cand. Sci (Med.), Assistant Professor, Department of Psychiatry and Addiction, Saint Petersburg State University</p></bio><bio xml:lang="ru"><p>к.м.н., Доцент кафедры психиатрии и наркологии Санкт-Петербургского государственного университета</p></bio><email>e.chumakov@spbu.ru</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">The Human artificial control Keren (HacK) lab, Azrieli Faculty of Medicine, Bar-Ilan University</institution></aff><aff><institution xml:lang="ru">Университет Бар-Илан</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Saint Petersburg State University</institution></aff><aff><institution xml:lang="ru">Санкт-Петербургский государственный университет</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2023-09-25" publication-format="electronic"><day>25</day><month>09</month><year>2023</year></pub-date><pub-date date-type="pub" iso-8601-date="2023-09-29" publication-format="electronic"><day>29</day><month>09</month><year>2023</year></pub-date><volume>4</volume><issue>3</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>33</fpage><lpage>42</lpage><history><date date-type="received" iso-8601-date="2023-06-08"><day>08</day><month>06</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2023-09-12"><day>12</day><month>09</month><year>2023</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2023, Vasilchenko K.F., Chumakov E.M.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2023, Васильченко К.Ф., Чумаков Е.М.</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Vasilchenko K.F., Chumakov E.M.</copyright-holder><copyright-holder xml:lang="ru">Васильченко К.Ф., Чумаков Е.М.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc-nd/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://consortium-psy.com/jour/article/view/11244">https://consortium-psy.com/jour/article/view/11244</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND: </bold>Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience, psychiatry, and computer science. It employs mathematical models and computational simulations to shed light on the complexities inherent to mental disorders.</p> <p><bold>AIM: </bold>The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, to discuss its significant challenges, as well as the potential opportunities for the field’s growth.</p> <p><bold>METHODS:</bold> The authors have carried out a narrative review of the scientific literature published on the topic of computational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholar databases. A descriptive analysis was used to summarize the published information on the theoretical and practical aspects of computational psychiatry.</p> <p><bold>RESULTS: </bold>The article relates the development of the scientific approach in computational psychiatry since the mid-1980s. The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining the mechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) are summarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.</p> <p><bold>CONCLUSION: </bold>The capacity of current computational technologies in psychiatry has reached a stage where its integration into psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are no longer rooted in technological advancement, but in ethics, education, and understanding.</p></abstract><trans-abstract xml:lang="ru"><p><bold>ВВЕДЕНИЕ: </bold>Вычислительная психиатрия — это область научных знаний, которая находится на пересечении нейронауки, психиатрии и информатики, использующая математические модели и вычислительные симуляции для понимания имеющихся сложностей в моделировании психических расстройств.</p> <p><bold>ЦЕЛЬ:</bold> Цель данного нарративного обзора — дать представление о текущем положении дел в области вычислительной психиатрии, обсудить ее существенные вызовы, а также потенциальные возможности для развития этой области.</p> <p><bold>МЕТОДЫ:</bold> Авторы провели обзор научной литературы, опубликованной по теме вычислительной психиатрии. Поиск литературы проводился в базах данных PubMed и eLibrary. Для обобщения опубликованной информации о теоретических и практических аспектах вычислительной психиатрии был использован описательный анализ.</p> <p><bold>РЕЗУЛЬТАТЫ:</bold> в статье описано развитие научного подхода в вычислительной психиатрии с середины 1980-х годов. Обобщены данные о практическом применении методов вычислительной психиатрии для моделирования психических расстройств и объяснения механизмов развития психопатологической симптоматики (при шизофрении, синдроме дефицита внимания/гиперактивности, расстройствах аутистического спектра, тревожных расстройствах, обсессивно-компульсивном расстройстве, расстройствах вследствие употребления психоактивных веществ). Обсуждаются проблемы, ограничения и будущие перспективы вычислительной психиатрии.</p> <p><bold>ЗАКЛЮЧЕНИЕ:</bold> Возможности современных вычислительных технологий в психиатрии достигли той стадии, когда их интеграция в психиатрическую практику не только возможна, но и крайне необходима. Препятствия, которые сейчас необходимо преодолеть, связаны не с технологическим прогрессом, а с этикой, образованием и пониманием технологий.</p></trans-abstract><kwd-group xml:lang="en"><kwd>computational psychiatry</kwd><kwd>artificial intelligence</kwd><kwd>machine learning</kwd><kwd>ethics</kwd><kwd>education</kwd><kwd>diagnosis of psychiatric disorders</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>вычислительная психиатрия</kwd><kwd>искусственный интеллект</kwd><kwd>машинное обучение</kwd><kwd>этика</kwd><kwd>образование</kwd><kwd>диагностика психических расстройств</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Montague PR, Dolan RJ, Friston KJ, Dayan P. Computational psychiatry. Trends Cogn Sci. 2012;16(1):72–80. doi: 10.1016/j.tics.2011.11.018.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Wang XJ, Krystal JH. Computational psychiatry. Neuron. 2014;84(3):638–54. doi: 10.1016/j.neuron.2014.10.018.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Friston KJ, Stephan KE, Montague R, Dolan RJ. Computational psychiatry: the brain as a phantastic organ. Lancet Psychiatry. 2014;1(2):148–58. doi: 10.1016/S2215-0366(14)70275-5.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Huys QJ, Moutoussis M, Williams J. Are computational models of any use to psychiatry? Neural Netw. 2011;24(6):544–51. doi: 10.1016/j.neunet.2011.03.001.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge, MA: MIT Press; 1998.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci. 2013;36(3):181–204. doi: 10.1017/S0140525X12000477.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Bassett DS, Zurn P, Gold JI. On the nature and use of models in network neuroscience. Nat Rev Neurosci. 2018;19(9):566–78. doi: 10.1038/s41583-018-0038-8.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Breakspear M. Dynamic models of large-scale brain activity. Nat Neurosci. 2017;20(3):340–52. doi: 10.1038/nn.4497.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci. 2015;16(3):159–72. doi: 10.1038/nrn3901.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>van den Heuvel MP, Sporns O. A cross-disorder connectome landscape of brain dysconnectivity. Nat Rev Neurosci. 2019;20(7):435–46. doi: 10.1038/s41583-019-0177-6.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Maia TV, Frank MJ. From reinforcement learning models to psychiatric and neurological disorders. Nat Neurosci. 2011;14(2):154–62. doi: 10.1038/nn.2723.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Stephan KE, Mathys C. Computational approaches to psychiatry. Curr Opin Neurobiol. 2014;25:85–92. doi: 10.1016/j.conb.2013.12.007.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Kessler RC. The costs of depression. Psychiatr Clin North Am. 2012;35(1):1–14. doi: 10.1016/j.psc.2011.11.005.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Woo CW, Chang LJ, Lindquist MA, Wager TD. Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci. 2017;20(3):365–77. doi: 10.1038/nn.4478.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Paulus MP. Pragmatism instead of mechanism: a call for impactful biological psychiatry. JAMA Psychiatry. 2015;72(7):631–2. doi: 10.1001/jamapsychiatry.2015.0497.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Ferrante M, Redish AD, Oquendo MA, Averbeck BB, Kinnane ME, Gordon JA. Computational psychiatry: a report from the 2017 NIMH workshop on opportunities and challenges. Mol Psychiatry. 2019;24(4):479–83. doi: 10.1038/s41380-018-0063-z.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–358. doi: 10.1056/NEJMra1814259.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–46. doi: 10.1093/bib/bbx044.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Luxton DD. AI decision-support: a dystopian future of machine paternalism? J Med Ethics. 2022;48(4):232–3. doi: 10.1136/medethics-2022-108243.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Vasilchenko KF, Drozdovsky YuV. Internalized stigma and social adaptation levels among patients with first episode schizophrenia. Siberian Herald of Psychiatry and Addiction Psychiatry. 2018;1(98):30–5. doi: 10.26617/1810- 3111-2018-1(98)-30-35. Russian.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Petrova NN, Fedotov IA, Chumakov EM. The analysis of the dynamics of psychiatrists‘ opinions on continuing medical education. V.M. Bekhterev review of psychiatry and medical psychology. 2019;2:102–7. doi: 10.31363/2313-7053-2019-2-102-107. Russian</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Vasilchenko KF, Chumakov EM. Comment on ‘Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry’. Int J Soc Psychiatry. 2023;0(0). doi: 10.1177/00207640231178464. Epub 2023 Jun 30.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Poldrack RA, Halchenko YO, Hanson SJ. Decoding the large-scale structure of brain function by classifying mental States across individuals. Psychol Sci. 2009;20(11):1364–72. doi: 10.1111/j.1467-9280.2009.02460.x.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537–51. doi: 10.1038/nrg3240.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Caspi A, Hariri AR, Holmes A, Uher R, Moffitt TE. Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits. Am J Psychiatry. 2010;167(5):509–27. doi: 10.1176/appi.ajp.2010.09101452.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Kendler KS, Neale MC. Endophenotype: a conceptual analysis. Mol Psychiatry. 2010;15(8):789–97. doi: 10.1038/mp.2010.8.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003 Apr;160(4):636–45. doi: 10.1176/appi.ajp.160.4.636.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Greist JH. Conservative radicalism: an approach to computers in mental health. In Schwanz MD, editor. Using computers in clinical practice: Psychotherapy and mental health applications. New York: The Haworth Press; 1984. p. 22–23.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Craig TJ. Overcoming clinicians’ resistance to computers. Hosp Community Psychiatry. 1984;35(2):121–2. doi: 10.1176/ps.35.2.121.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Desaire H. How (not) to generate a highly predictive biomarker panel using machine learning. J Proteome Res. 2022;21(9):2071–4. doi: 10.1021/acs.jproteome.2c00117.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Levman J, Ewenson B, Apaloo J, Berger D, Tyrrell PN. Error consistency for machine learning evaluation and validation with application to biomedical diagnostics. Diagnostics (Basel). 2023;13(7):1315. doi: 10.3390/diagnostics13071315.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Plass M, Kargl M, Kiehl TR, Regitnig P, Geißler C, Evans T, Zerbe N, Carvalho R, Holzinger A, Müller H. Explainability and causability in digital pathology. J Pathol Clin Res. 2023;9(4):251–60. doi: 10.1002/cjp2.322.</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Portacolone E, Halpern J, Luxenberg J, Harrison KL, Covinsky KE. Ethical issues raised by the introduction of artificial companions to older adults with cognitive impairment: a call for interdisciplinary collaborations. J Alzheimers Dis. 2020;76(2):445–55. doi: 10.3233/JAD-190952.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Hedlund JL, Vieweg BW, Cho DW. Mental health computing in the 1980s. Comput Hum Services. 1985;1(2):1–31. doi: 10.1300/j407v01n02_01.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Moutoussis M, Bentall RP, El-Deredy W, Dayan P. Bayesian modelling of Jumping-to-Conclusions bias in delusional patients. Cogn Neuropsychiatry. 2011;16(5):422–47. doi: 10.1080/13546805.2010.548678.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Murray GK, Corlett PR, Clark L, Pessiglione M, Blackwell AD, Honey G, Jones PB, Bullmore ET, Robbins TW, Fletcher PC. Substantia nigra/ventral tegmental reward prediction error disruption in psychosis. Mol Psychiatry. 2008;13(3):239, 267–76. doi: 10.1038/sj.mp.4002058.</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Gold JM, Waltz JA, Matveeva TM, Kasanova Z, Strauss GP, Herbener ES, Collins AG, Frank MJ. Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence. Arch Gen Psychiatry. 2012;69(2):129–38. doi: 10.1001/archgenpsychiatry.2011.1269.</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Adams RA, Stephan KE, Brown HR, Frith CD, Friston KJ. The computational anatomy of psychosis. Front Psychiatry. 2013;4:47. doi: 10.3389/fpsyt.2013.00047.</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Huys QJ, Pizzagalli DA, Bogdan R, Dayan P. Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biol Mood Anxiety Disord. 2013;3(1):12. doi: 10.1186/2045-5380-3-12.</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Rutledge RB, Chekroud AM, Huys QJ. Machine learning and big data in psychiatry: toward clinical applications. Curr Opin Neurobiol. 2019;55:152–9. doi: 10.1016/j.conb.2019.02.006.</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Goldway N, Eldar E, Shoval G, Hartley CA. Computational mechanisms of addiction and anxiety: a developmental perspective. Biol Psychiatry. 2023;93(8):739–50. doi: 10.1016/j.biopsych.2023.02.004.</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>van de Cruys S, Evers K, van der Hallen R, van Eylen L, Boets B, de-Wit L, Wagemans J. Precise minds in uncertain worlds: predictive coding in autism. Psychol Rev. 2014;121(4):649–75. doi: 10.1037/a0037665.</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Vaghi MM, Vértes PE, Kitzbichler MG, Apergis-Schoute AM, van der Flier FE, Fineberg NA, Sule A, Zaman R, Voon V, Kundu P, Bullmore ET, Robbins TW. Specific frontostriatal circuits for impaired cognitive flexibility and goal-directed planning in obsessive-compulsive disorder: evidence from resting-state functional connectivity. Biol Psychiatry. 2017;81(8):708–17. doi: 10.1016/j.biopsych.2016.08.009.</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Voon V, Derbyshire K, Rück C, Irvine MA, Worbe Y, Enander J, Schreiber LR, Gillan C, Fineberg NA, Sahakian BJ, Robbins TW, Harrison NA, Wood J, Daw ND, Dayan P, Grant JE, Bullmore ET. Disorders of compulsivity: a common bias towards learning habits. Mol Psychiatry. 2015;20(3):345–52. doi: 10.1038/mp.2014.44.</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Grupe DW, Nitschke JB. Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective. Nat Rev Neurosci. 2013;14(7):488–501. doi: 10.1038/nrn3524.</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>Paulus MP, Thompson WK. Computational approaches and machine learning for individual-level treatment predictions. Psychopharmacology (Berl). 2021;238(5):1231–9. doi: 10.1007/s00213-019-05282-4.</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>Frank GK, Shott ME, Riederer J, Pryor TL. Altered structural and effective connectivity in anorexia and bulimia nervosa in circuits that regulate energy and reward homeostasis. Transl Psychiatry. 2016;6(11):e932. doi: 10.1038/tp.2016.199.</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>Paulus MP, Huys QJ, Maia TV. A roadmap for the development of applied computational psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(5):386–92. doi: 10.1016/j.bpsc.2016.05.001.</mixed-citation></ref><ref id="B49"><label>49.</label><mixed-citation>Bogacz R. A tutorial on the free-energy framework for modelling perception and learning. J Math Psychol. 2017;76(Pt B):198–211. doi: 10.1016/j.jmp.2015.11.003.</mixed-citation></ref><ref id="B50"><label>50.</label><mixed-citation>Adams RA, Huys QJ, Roiser JP. Computational Psychiatry: towards a mathematically informed understanding of mental illness. J Neurol Neurosurg Psychiatry. 2016;87(1):53–63. doi: 10.1136/jnnp-2015-310737.</mixed-citation></ref><ref id="B51"><label>51.</label><mixed-citation>Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci. 2017;12(6):1100–22. doi: 10.1177/1745691617693393.</mixed-citation></ref><ref id="B52"><label>52.</label><mixed-citation>Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121. doi: 10.1146/annurev-clinpsy-050212-185608.</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>Dyson M. Combatting AI’s protectionism &amp; totalitarian-coded hypnosis: the case for ai reparations &amp; antitrust remedies in the ecology of collective self-determination. SMU Law Review. 2022;25:625–722. doi: 10.25172/smulr.75.3.7.</mixed-citation></ref><ref id="B54"><label>54.</label><mixed-citation>Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9. doi: 10.1016/S0140-6736(12)62129-1.</mixed-citation></ref><ref id="B55"><label>55.</label><mixed-citation>Vasilchenko KF. [Self-stigmatization, adaptation and quality of life of persons with the first psychotic episode of schizophrenia (typology, rehabilitation, prevention)] [dissertation]. Omsk; 2019. p. 232. Russian.</mixed-citation></ref><ref id="B56"><label>56.</label><mixed-citation>Bhugra D, Smith A, Ventriglio A, Hermans MHM, Ng R, Javed A, Chumakov E, Kar A, Ruiz R, Oquendo M, Chisolm MS, Werneke U, Suryadevara U, Jibson M, Hobbs J, Castaldelli-Maia J, Nair M, Seshadri S, Subramanyam A, Patil N, Chandra P, Liebrenz M. World psychiatric association-asian journal of psychiatry commission on psychiatric education in the 21st century. Asian J Psychiatr. 2023;88:103739. doi: 10.1016/j.ajp.2023.103739.</mixed-citation></ref><ref id="B57"><label>57.</label><mixed-citation>Kibitov AA, Chumakov EM, Nechaeva AI, Sorokin MY, Petrova NN, Vetrova MV. Professional values and educational needs among mental health specialists in Russia: survey results. Consortium Psychiatricum. 2022;3(3):36–45. doi: 10.17816/CP184.</mixed-citation></ref></ref-list></back></article>
