Understanding as a bottleneck for the data-driven approach to psychiatric science
PDF

Keywords

Big data
Machine learning
Mental illness
Psychiatry
Recovery
Understanding

How to Cite

Crook, B. (2023). Understanding as a bottleneck for the data-driven approach to psychiatric science. Philosophy and the Mind Sciences, 4. https://doi.org/10.33735/phimisci.2023.9658

Abstract

The data-driven approach to psychiatric science leverages large volumes of patient data to construct machine learning models with the goal of optimizing clinical decision making. Advocates claim that this methodology is well-placed to deliver transformative improvements to psychiatric science. I argue that talk of a data-driven revolution in psychiatry is premature. Transformative improvements, cashed out in terms of better patient outcomes, cannot be achieved without addressing patient understanding. That is, how patients understand their own mental illnesses. I conceptualize understanding as the possession of adaptive mental constructs through which experience is mediated. I suggest that this notion of understanding serves as a bottleneck which any prospective approach to psychiatry must address to be efficacious. Subsequently I argue that, though the data-driven approach is undoubtedly powerful, it does not have a straightforward means of unblocking the bottleneck of understanding. I suggest that the data-driven approach must be supplemented with significant theoretical progress if it is to transform psychiatry.

https://doi.org/10.33735/phimisci.2023.9658
PDF

References

Alfattni, G., Peek, N., & Nenadic, G. (2020). Extraction of temporal relations from clinical free text: A systematic review of current approaches. Journal of Biomedical Informatics, 108, 103488. https://doi.org/10.1016/j.jbi.2020.103488

Anthony, W. A. (2000). A recovery-oriented service system: Setting some system level standards. Psychiatric Rehabilitation Journal, 24(2), 159–168. https://doi.org/10.1037/h0095104

Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017). Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage, 145(Pt B), 137–165. https://doi.org/10.1016/j.neuroimage.2016.02.079

Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. (2021). Ecological momentary interventions for mental health: A scoping review. PLOS ONE, 16(3), e0248152. https://doi.org/10.1371/journal.pone.0248152

Banner, N. F. (2013). Mental disorders are not brain disorders. Journal of Evaluation in Clinical Practice, 19(3), 509–513. https://doi.org/10.1111/jep.12048

Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1–23. https://doi.org/10.1093/scan/nsw154

Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., … Pascanu, R. (2018). Relational inductive biases, deep learning, and graph networks. ArXiv:1806.01261 [Cs, Stat]. http://arxiv.org/abs/1806.01261

Beck, A. T. (1985). Cognitive Therapy, Behavior Therapy, Psychoanalysis, and Pharmacotherapy. In M. J. Mahoney & A. Freeman (Eds.), Cognition and Psychotherapy (pp. 325–347). Springer US. https://doi.org/10.1007/978-1-4684-7562-3_14

Beck, A. T., & Haigh, E. A. P. (2014). Advances in cognitive theory and therapy: The generic cognitive model. Annual Review of Clinical Psychology, 10, 1–24. https://doi.org/10.1146/annurev-clinpsy-032813-153734

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. https://doi.org/10.1109/tpami.2013.50

Bennett, D., Silverstein, S. M., & Niv, Y. (2019). The Two Cultures of Computational Psychiatry. JAMA Psychiatry, 76(6), 563–564. https://doi.org/10.1001/jamapsychiatry.2019.0231

Bickman, L. (2020). Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Administration and Policy in Mental Health, 47(5), 795–843. https://doi.org/10.1007/s10488-020-01065-8

Borsboom, D. (2017). Mental disorders, network models, and dynamical systems. In K. S. Kendler & J. Parnas (Eds.), Philosophical Issues in Psychiatry IV: Psychiatric Nosology (pp. 80–97). Oxford University Press.

Borsboom, D., Cramer, A. O. J., & Kalis, A. (2019). Brain disorders? Not really: Why network structures block reductionism in psychopathology research. Behavioral and Brain Sciences, 42, e2. https://doi.org/10.1017/S0140525X17002266

Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021). Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. ArXiv:2104.13478 [Cs, Stat]. http://arxiv.org/abs/2104.13478

Brown, C., Story, G. W., Mourão-Miranda, J., & Baker, J. T. (2021). Will artificial intelligence eventually replace psychiatrists? The British Journal of Psychiatry, 218(3), 131–134. https://doi.org/10.1192/bjp.2019.245

Bullmore, E., Fletcher, P., & Jones, P. B. (2009). Why psychiatry can’t afford to be neurophobic. The British Journal of Psychiatry, 194(4), 293–295. https://doi.org/10.1192/bjp.bp.108.058479

Burgess, R. A., Jain, S., Petersen, I., & Lund, C. (2020). Social interventions: A new era for global mental health? The Lancet Psychiatry, 7(2), 118–119. https://doi.org/10.1016/S2215-0366(19)30397-9

Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3. https://doi.org/10.1016/j.bpsc.2017.11.007

Cearns, M., Hahn, T., & Baune, B. T. (2019). Recommendations and future directions for supervised machine learning in psychiatry. Translational Psychiatry, 9(1), 1–12. https://doi.org/10.1038/s41398-019-0607-2

Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D., & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 20(2), 154–170. https://doi.org/10.1002/wps.20882

Chollet, F. (2021). Deep Learning with Python, Second Edition. Simon and Schuster.

Cichy, R. M., & Kaiser, D. (2019). Deep Neural Networks as Scientific Models. Trends in Cognitive Sciences, 23(4), 305–317. https://doi.org/10.1016/j.tics.2019.01.009

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477

Cuevas, C. D. las, Peñate, W., & Rivera, L. de. (2014). To what extent is treatment adherence of psychiatric patients influenced by their participation in shared decision making? Patient Preference and Adherence, 8, 1547–1553. https://doi.org/10.2147/PPA.S73029

Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11(1), 126. https://doi.org/10.1186/1741-7015-11-126

Daly, A., & Gallagher, S. (2019). Towards a Phenomenology of Self-Patterns in Psychopathological Diagnosis and Therapy. Psychopathology, 52(1), 33–49. https://doi.org/10.1159/000499315

Dehaene, S. (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. Penguin.

Deng, L., & Liu, Y. (2018). A Joint Introduction to Natural Language Processing and to Deep Learning. In L. Deng & Y. Liu (Eds.), Deep Learning in Natural Language Processing (pp. 1–22). Springer. https://doi.org/10.1007/978-981-10-5209-5_1

Department of Health and Social Care. (2021, March 27). Mental health recovery plan backed by £500 million. GOV.UK. https://www.gov.uk/government/news/mental-health-recovery-plan-backed-by-500-million

Dickinson, E. (1998). The Poems of Emily Dickinson. Harvard University Press. (Original work published 1890)

Dilthey, W. (1977). Ideas Concerning a Descriptive and Analytic Psychology (1894). In W. Dilthey (Ed.), & R. M. Zaner (Trans.), Descriptive Psychology and Historical Understanding (pp. 21–120). Springer Netherlands. https://doi.org/10.1007/978-94-009-9658-8_2 (Original work published 1894)

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755

Durstewitz, D., Koppe, G., & Meyer-Lindenberg, A. (2019). Deep neural networks in psychiatry. Molecular Psychiatry, 24(11), 1583–1598. https://doi.org/10.1038/s41380-019-0365-9

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual Review of Clinical Psychology, 14, 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037

Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., … Olah, C. (2021). A Mathematical Framework for Transformer Circuits. Transformer Circuits Thread.

Elujide, I., Fashoto, S. G., Fashoto, B., Mbunge, E., Folorunso, S. O., & Olamijuwon, J. O. (2021). Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases. Informatics in Medicine Unlocked, 23, 100545. https://doi.org/10.1016/j.imu.2021.100545

Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science (New York, N.Y.), 196(4286), 129–136. https://doi.org/10.1126/science.847460

Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D. (2020). Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning. JAMA Psychiatry, 77(1), 35–43. https://doi.org/10.1001/jamapsychiatry.2019.2664

Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019). The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes. Trends in Cognitive Sciences, 23(7), 584–601. https://doi.org/10.1016/j.tics.2019.03.009

Fernandes, B. S., Williams, L. M., Steiner, J., Leboyer, M., Carvalho, A. F., & Berk, M. (2017). The new field of ‘precision psychiatry’. BMC Medicine, 15(1), 80. https://doi.org/10.1186/s12916-017-0849-x

Frank, J. D., & Frank, J. B. (1993). Persuasion and Healing: A Comparative Study of Psychotherapy. JHU Press.

García-Gutiérrez, M. S., Navarrete, F., Sala, F., Gasparyan, A., Austrich-Olivares, A., & Manzanares, J. (2020). Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality. Frontiers in Psychiatry, 11, 432. https://doi.org/10.3389/fpsyt.2020.00432

Ghaemi, S. N. (2007). Existence and pluralism: The rediscovery of Karl Jaspers. Psychopathology, 40(2), 75–82. https://doi.org/10.1159/000098487

Gillan, C. M., & Whelan, R. (2017). What big data can do for treatment in psychiatry. Current Opinion in Behavioral Sciences, 18, 34–42. https://doi.org/10.1016/j.cobeha.2017.07.003

Glover, J. (2020). Psychiatry, folk psychology, and the impact of neuroscience—A response to Steve Hyman’s Loebel Lectures. In essor J. Savulescu, R. Roache, W. Davies, & essor J. P. Loebel (Eds.), Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 301–319). Oxford University Press.

Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2016). What does research reproducibility mean? Science Translational Medicine, 8(341), 341ps12. https://doi.org/10.1126/scitranslmed.aaf5027

Gould, I. C., Shepherd, A. M., Laurens, K. R., Cairns, M. J., Carr, V. J., & Green, M. J. (2014). Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach. NeuroImage: Clinical, 6, 229–236. https://doi.org/10.1016/j.nicl.2014.09.009

Harmer, C. J., & Cowen, P. J. (2017). How do drugs for psychiatric disorders work? Epidemiology and Psychiatric Sciences, 27(2), 141–142. https://doi.org/10.1017/S204579601700066X

Hengartner, M. P., & Lehmann, S. N. (2017). Why Psychiatric Research Must Abandon Traditional Diagnostic Classification and Adopt a Fully Dimensional Scope: Two Solutions to a Persistent Problem. Frontiers in Psychiatry, 8, 101. https://doi.org/10.3389/fpsyt.2017.00101

Hey, T., Butler, K., Jackson, S., & Thiyagalingam, J. (2020). Machine learning and big scientific data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378(2166), 20190054. https://doi.org/10.1098/rsta.2019.0054

Hyman, S. E. (2010). The Diagnosis of Mental Disorders: The Problem of Reification. Annual Review of Clinical Psychology, 6(1), 155–179. https://doi.org/10.1146/annurev.clinpsy.3.022806.091532

Hyman, S. E., & McConnell, D. (2020). Mental illness: The collision of meaning with mechanism. In Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 263–289). Oxford University Press. https://oxfordmedicine.com/view/10.1093/med/9780198789697.001.0001/med-9780198789697

Insel, T. R., Collins, P. Y., & Hyman, S. E. (2015). Darkness Invisible: The Hidden Global Costs of Mental Illness. Foreign Affairs, 94(1), 127–135.

Insel, T. R., & Cuthbert, B. N. (2015). Medicine. Brain disorders? Precisely. Science (New York, N.Y.), 348(6234), 499–500. https://doi.org/10.1126/science.aab2358

Ivanov, I., & Schwartz, J. M. (2021). Why Psychotropic Drugs Don’t Cure Mental Illness—But Should They? Frontiers in Psychiatry, 12. https://www.frontiersin.org/article/10.3389/fpsyt.2021.579566

Jacob, K. S. (2015). Recovery Model of Mental Illness: A Complementary Approach to Psychiatric Care. Indian Journal of Psychological Medicine, 37(2), 117–119. https://doi.org/10.4103/0253-7176.155605

Jaspers, K. (1997). General Psychopathology (J. Hoenig & M. Hamilton, Trans.). JHU Press. (Original work published 1959)

Johansson, H., & Eklund, M. (2003). Patients’ opinion on what constitutes good psychiatric care. Scandinavian Journal of Caring Sciences, 17(4), 339–346. https://doi.org/10.1046/j.0283-9318.2003.00233.x

Joober, R., & Tabbane, K. (2019). From the neo-Kraepelinian framework to the new mechanical philosophy of psychiatry: Regaining common sense. Journal of Psychiatry & Neuroscience : JPN, 44(1), 3–7. https://doi.org/10.1503/jpn.180240

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2

Kamenov, K., Twomey, C., Cabello, M., Prina, A. M., & Ayuso-Mateos, J. L. (2017). The efficacy of psychotherapy, pharmacotherapy and their combination on functioning and quality of life in depression: A meta-analysis. Psychological Medicine, 47(3), 414–425. https://doi.org/10.1017/S0033291716002774

Kawachi, I., & Berkman, L. F. (2001). Social ties and mental health. Journal of Urban Health : Bulletin of the New York Academy of Medicine, 78(3), 458–467. https://doi.org/10.1093/jurban/78.3.458

Kendler, K. S. (2012). Levels of explanation in psychiatric and substance use disorders: Implications for the development of an etiologically based nosology. Molecular Psychiatry, 17(1), 11–21. https://doi.org/10.1038/mp.2011.70

Kendler, K. S., & Campbell, J. (2014). Expanding the domain of the understandable in psychiatric illness: An updating of the Jasperian framework of explanation and understanding. Psychological Medicine, 44(1), 1–7. https://doi.org/10.1017/S0033291712003030

Kendler, K. S., & Gyngell, C. (2020). Multilevel interactions and the dappled causal world of psychiatric disorders. In Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 25–45). Oxford University Press. https://oxfordmedicine.com/view/10.1093/med/9780198789697.001.0001/med-9780198789697

Keogh, E., & Mueen, A. (2017). Curse of Dimensionality. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 314–315). Springer US. https://doi.org/10.1007/978-1-4899-7687-1_192

Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., Ruef, A., Kambeitz-Ilankovic, L., Antonucci, L. A., Neufang, S., Schmidt-Kraepelin, C., Ruhrmann, S., Penzel, N., Kambeitz, J., Haidl, T. K., … Meisenzahl, E. (2021). Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry, 78(2), 1–15. https://doi.org/10.1001/jamapsychiatry.2020.3604

Krupnick, J. L., Sotsky, S. M., Elkin, I., Simmens, S., Moyer, J., Watkins, J., & Pilkonis, P. A. (2006). The Role of the Therapeutic Alliance in Psychotherapy and Pharmacotherapy Outcome: Findings in the National Institute of Mental Health Treatment of Depression Collaborative Research Program. FOCUS, 4(2), 269–277. https://doi.org/10.1176/foc.4.2.269

Kumazaki, T. (2013). The theoretical root of Karl Jaspers’ General Psychopathology. Part 1: Reconsidering the influence of phenomenology and hermeneutics. History of Psychiatry, 24(2), 212–226. https://doi.org/10.1177/0957154X13476201

Leichsenring, F., Steinert, C., Rabung, S., & Ioannidis, J. P. A. (2022). The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: An umbrella review and meta-analytic evaluation of recent meta-analyses. World Psychiatry, 21(1), 133–145. https://doi.org/10.1002/wps.20941

Leventhal, H., Brissette, I., & Leventhal, E. A. (2003). The common-sense model of self-regulation of health and illness. In The self-regulation of health and illness behaviour (pp. 42–65). Routledge.

Levy, N. (2020). The truth in social construction. In Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 97–108). Oxford University Press. https://oxfordmedicine.com/view/10.1093/med/9780198789697.001.0001/med-9780198789697

Lindquist, K. A., MacCormack, J. K., & Shablack, H. (2015). The role of language in emotion: Predictions from psychological constructionism. Frontiers in Psychology, 6. https://www.frontiersin.org/article/10.3389/fpsyg.2015.00444

Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020). Emergent linguistic structure in artificial neural networks trained by self-supervision. Proceedings of the National Academy of Sciences, 117(48), 30046–30054. https://doi.org/10.1073/pnas.1907367117

McConnell, D. (2020). The proper place of subjectivity, meaning, and folk psychology in psychiatry. In Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 290–303). Oxford University Press. https://oxfordmedicine.com/view/10.1093/med/9780198789697.001.0001/med-9780198789697

McConnell, D., & Snoek, A. (2018). The Importance of Self-Narration in Recovery From Addiction. Philosophy, Psychiatry, and Psychology, 25(3), 31–44. https://doi.org/10.1353/ppp.2018.0022

McGorry, P. D., Hartmann, J. A., Spooner, R., & Nelson, B. (2018). Beyond the “at risk mental state” concept: Transitioning to transdiagnostic psychiatry. World Psychiatry, 17(2), 133–142. https://doi.org/10.1002/wps.20514

McGorry, P. D., & Nelson, B. (2019). Transdiagnostic psychiatry: Premature closure on a crucial pathway to clinical utility for psychiatric diagnosis. World Psychiatry, 18(3), 359–360. https://doi.org/10.1002/wps.20679

McHugh, R. K., Whitton, S. W., Peckham, A. D., Welge, J. A., & Otto, M. W. (2013). Patient Preference for Psychological vs Pharmacologic Treatment of Psychiatric Disorders: A Meta-Analytic Review. The Journal of Clinical Psychiatry, 74(6), 13979. https://doi.org/10.4088/JCP.12r07757

Middleton, H., & Moncrieff, J. (2019). Critical psychiatry: A brief overview. BJPsych Advances, 25(1), 47–54. https://doi.org/10.1192/bja.2018.38

Miranda, L., Paul, R., Pütz, B., Koutsouleris, N., & Müller-Myhsok, B. (2021). Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Frontiers in Psychiatry, 12, 665536. https://doi.org/10.3389/fpsyt.2021.665536

Murphy, D. (2006). Psychiatry in the Scientific Image. MIT Press.

Najafpour, Z., Fatemi, A., Goudarzi, Z., Goudarzi, R., Shayanfard, K., & Noorizadeh, F. (2021). Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis. Journal of Neuroradiology = Journal De Neuroradiologie, 48(5), 348–358. https://doi.org/10.1016/j.neurad.2020.12.003

Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018). The Building Blocks of Interpretability. Distill, 3(3), 10.23915/distill.00010. https://doi.org/10.23915/distill.00010

Olfson, M., & Marcus, S. C. (2010). National Trends in Outpatient Psychotherapy. American Journal of Psychiatry, 167(12), 1456–1463. https://doi.org/10.1176/appi.ajp.2010.10040570

Paulus, M. P. (2015). Pragmatism Instead of Mechanism: A Call for Impactful Biological Psychiatry. JAMA Psychiatry, 72(7), 631–632. https://doi.org/10.1001/jamapsychiatry.2015.0497

Pelin, H., Ising, M., Stein, F., Meinert, S., Meller, T., Brosch, K., Winter, N. R., Krug, A., Leenings, R., Lemke, H., Nenadić, I., Heilmann-Heimbach, S., Forstner, A. J., Nöthen, M. M., Opel, N., Repple, J., Pfarr, J., Ringwald, K., Schmitt, S., … Andlauer, T. F. M. (2021). Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 46(11), 1895–1905. https://doi.org/10.1038/s41386-021-01051-0

Petrie, K. J., Broadbent, E., & Kydd, R. (2008). Illness perceptions in mental health: Issues and potential applications. Journal of Mental Health, 17(6), 559–564. https://doi.org/10.1080/09638230802523047

Plana-Ripoll, O., Pedersen, C. B., Holtz, Y., Benros, M. E., Dalsgaard, S., de Jonge, P., Fan, C. C., Degenhardt, L., Ganna, A., Greve, A. N., Gunn, J., Iburg, K. M., Kessing, L. V., Lee, B. K., Lim, C. C. W., Mors, O., Nordentoft, M., Prior, A., Roest, A. M., … McGrath, J. J. (2019). Exploring Comorbidity Within Mental Disorders Among a Danish National Population. JAMA Psychiatry, 76(3), 259–270. https://doi.org/10.1001/jamapsychiatry.2018.3658

Prosser, A., Helfer, B., & Leucht, S. (2016). Biological v. psychosocial treatments: A myth about pharmacotherapy v. psychotherapy. The British Journal of Psychiatry, 208(4), 309–311. https://doi.org/10.1192/bjp.bp.115.178368

Qureshi, M. N. I., Min, B., Jo, H. J., & Lee, B. (2016). Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study. PLOS ONE, 11(8), e0160697. https://doi.org/10.1371/journal.pone.0160697

Ramon, S., Healy, B., & Renouf, N. (2007). Recovery from Mental Illness as an Emergent Concept and Practice in Australia and the UK. International Journal of Social Psychiatry, 53(2), 108–122. https://doi.org/10.1177/0020764006075018

Rogers, C. R. (1951). Client-centered therapy; its current practice, implications, and theory (pp. xii, 560). Houghton Mifflin.

Russell, S. J., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. https://doi.org/10.5860/choice.33-1577

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

Sathyanarayana Rao, T. S., & Andrade, C. (2016). Classification of psychotropic drugs: Problems, solutions, and more problems. Indian Journal of Psychiatry, 58(2), 111–113. https://doi.org/10.4103/0019-5545.183771

Sheu, Y.-H. (2020). Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research. Frontiers in Psychiatry, 11, 551299. https://doi.org/10.3389/fpsyt.2020.551299

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604. https://doi.org/10.1109/JBHI.2017.2767063

Shorter, E. (1997). A history of psychiatry: From the era of the asylum to the age of Prozac (pp. xii, 436). John Wiley & Sons.

Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021). Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. Journal of Biomedical Informatics, 115, 103671. https://doi.org/10.1016/j.jbi.2020.103671

Smith, G. C., & Strain, J. J. (2002). George Engel’s contribution to clinical psychiatry. The Australian and New Zealand Journal of Psychiatry, 36(4), 458–466. https://doi.org/10.1046/j.1440-1614.2002.t01-1-01036.x

Stanghellini, G., & Broome, M. R. (2014). Psychopathology as the basic science of psychiatry. The British Journal of Psychiatry: The Journal of Mental Science, 205(3), 169–170. https://doi.org/10.1192/bjp.bp.113.138974

Suhara, Y., Xu, Y., & Pentland, A. ‘Sandy’. (2017). DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. Proceedings of the 26th International Conference on World Wide Web, 715–724. https://doi.org/10.1145/3038912.3052676

Szasz, T. S. (1960). The myth of mental illness. American Psychologist, 15(2), 113–118. https://doi.org/10.1037/h0046535

Thornton, T. (2020). Psychiatry’s inchoate wish for a paradigm shift and the biopsychosocial model of mental illness. In Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine (pp. 229–239). Oxford University Press. https://oxfordmedicine.com/view/10.1093/med/9780198789697.001.0001/med-9780198789697

Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. Proceedings of the 4th Machine Learning for Healthcare Conference, 359–380. https://proceedings.mlr.press/v106/tonekaboni19a.html

Trivedi, M. H. (2016). Right patient, right treatment, right time: Biosignatures and precision medicine in depression. World Psychiatry, 15(3), 237–238. https://doi.org/10.1002/wps.20371

Turkheimer, Eric. (2017). I bet on Borsboom. In K. S. Kendler & J. Parnas (Eds.), Philosophical Issues in Psychiatry IV: Psychiatric Nosology (pp. 98–100). Oxford University Press.

Vigo, D., Thornicroft, G., & Atun, R. (2016). Estimating the true global burden of mental illness. The Lancet. Psychiatry, 3(2), 171–178. https://doi.org/10.1016/S2215-0366(15)00505-2

Wakefield, J. C. (1992). The concept of mental disorder. On the boundary between biological facts and social values. The American Psychologist, 47(3), 373–388. https://doi.org/10.1037//0003-066x.47.3.373

Walter, H. (2013). The Third Wave of Biological Psychiatry. Frontiers in Psychology, 4. https://www.frontiersin.org/article/10.3389/fpsyg.2013.00582

Wampold, B. E. (2015). How important are the common factors in psychotherapy? An update. World Psychiatry, 14(3), 270–277. https://doi.org/10.1002/wps.20238

Wampold, B. E., & Budge, S. L. (2012). The 2011 Leona Tyler Award Address: The Relationship—and Its Relationship to the Common and Specific Factors of Psychotherapy. The Counseling Psychologist, 40(4), 601–623. https://doi.org/10.1177/0011000011432709

Wampold, B. E., & Imel, Z. E. (2015). The great psychotherapy debate: The evidence for what makes psychotherapy work, 2nd ed (pp. x, 323). Routledge/Taylor & Francis Group.

Well Being Trust. (2020, December 16). A Unified Vision for the Future of Mental Health, Addiction, and Well-Being in the United States. Well Being Trust. https://wellbeingtrust.org/press-releases/ceos-from-14-top-mental-health-organizations-join-together/

WHO. (2019, May 2). Special initiative for mental health (2019–2023). https://www.who.int/publications-detail-redirect/special-initiative-for-mental-health-(2019-2023)

Wilkenfeld, D. A. (2013). Understanding as representation manipulability. Synthese, 190(6), 997–1016. https://doi.org/10.1007/s11229-011-0055-x

Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: Brain models in translational neuroimaging. Nature Neuroscience, 20(3), 365–377. https://doi.org/10.1038/nn.4478

World Economic Forum. (2019, July 1). Empowering 8 Billion Minds: Enabling Better Mental Health for All via the Ethical Adoption of Technologies. World Economic Forum. https://www.weforum.org/whitepapers/empowering-8-billion-minds-enabling-better-mental-health-for-all-via-the-ethical-adoption-of-technologies/

Yamins, D. L. K., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19(3), 356–365. https://doi.org/10.1038/nn.4244

Zachar, P., & Kendler, K. S. (2017). The Philosophy of Nosology. Annual Review of Clinical Psychology, 13, 49–71. https://doi.org/10.1146/annurev-clinpsy-032816-045020

Zahavi, D., & Loidolt, S. (2022). Critical phenomenology and psychiatry. Continental Philosophy Review, 55(1), 55–75. https://doi.org/10.1007/s11007-021-09553-w

Zednik, C. (2021). Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence. Philosophy & Technology, 34(2), 265–288. https://doi.org/10.1007/s13347-019-00382-7

Zilcha-Mano, S., Roose, S. P., Barber, J. P., & Rutherford, B. R. (2015). Therapeutic Alliance in Antidepressant Treatment: Cause or Effect of Symptomatic Levels? Psychotherapy and Psychosomatics, 84(3), 177–182. https://doi.org/10.1159/000379756

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2023 Barnaby Crook