Skip to main content
SpringerLink
Log in

Mind-Reading Machines: Promises, Pitfalls, and Solutions of Implementing Machine Learning in Mental Health

  • Chapter
  • First Online:
AI, Consciousness and The New Humanism

  • 68 Accesses

Abstract

The central premise of implementing machines to understand minds is perhaps based on Emerson Pugh’s (in)famous quote: “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t”. This circular paradox has led us to the quest for that quintessential ‘mastermind’ or ‘master machine’ that can unravel the mysteries of our minds. An important stepping stone towards this understanding is to examine how perceptrons—models of neurons in artificial neural networks—can help to decode processes that underlie disorders of the mind. This chapter focuses on the rapidly growing applications of machine learning techniques to model and predict human behaviour in a clinical setting. Mental disorders continue to remain an enigma and most discoveries, therapeutic or neurobiological, stem from serendipity. Although the surge in neuroscience over the last decade has certainly strengthened the foundations of understanding mental illness, we have just started to rummage at the tip of the iceberg. We critically review the applied aspects of artificial intelligence and machine learning in decoding important clinical outcomes in psychiatry. From predicting the onset of psychotic disorders to classifying mental disorders, long-range applications have been proposed and examined. The veridicality and implementation of these results in real-world settings will also be examined. We then highlight the promises, challenges, and potential solutions of implementing these operations to better model mental disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020). Effectiveness and safety of using chatbots to improve mental health: Systematic review and meta-analysis. Journal of Medical Internet Research, 22, e16021. https://doi.org/10.2196/16021

    Article  Google Scholar 

  • American Psychiatric Association (Ed.) (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). American Psychiatric Association.

    Google Scholar 

  • Bathina, K. C., ten Thij, M., Lorenzo-Luaces, L., Rutter, L. A., & Bollen, J. (2021). Individuals with depression express more distorted thinking on social media. Nature Human Behaviour, 5, 458–466. https://doi.org/10.1038/s41562-021-01050-7

    Article  Google Scholar 

  • Bedi, G., Carrillo, F., Cecchi, G.A., Slezak, D. F., Sigman, M., Mota, N. B., Ribeiro, S., Javitt, D. C., Copelli, M., & Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophrenia, 1. https://doi.org/10.1038/npjschz.2015.30

  • Berezutskaya, J., Saive, A.-L., Jerbi, K., & van Gerven, M. (2022). How does artificial intelligence contribute to iEEG research? https://doi.org/10.48550/arXiv.2207.13190

  • Bethlehem, R. A. L., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., et al. (2022). Brain charts for the human lifespan. Nature, 604, 525–533. https://doi.org/10.1038/s41586-022-04554-y

  • Bittar, A., Velupillai, S., Roberts, A., & Dutta, R. (2021). Using general-purpose sentiment lexicons for suicide risk assessment in electronic health records: corpus-based analysis. JMIR Medical Informatics, 9, e22397. https://doi.org/10.2196/22397

    Article  Google Scholar 

  • Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies, superintelligence: Paths, dangers, strategies. Oxford University Press.

    Google Scholar 

  • Boushell, L. W., Shugars, D. A., & Eidson, R. S. (2019). Patient assessment, examination, diagnosis, and treatment planning. In A.V. Ritter, L.W. Boushell & R. Walter (Eds.), Sturdevant’s art and science of operative dentistry (pp. 95–119). Elsevier. https://doi.org/10.1016/B978-0-323-47833-5.00003-4

  • Bousman, C. A., Yung, A. R., Pantelis, C., Ellis, J. A., Chavez, R. A., Nelson, B., Lin, A., Wood, S. J., Amminger, G. P., Velakoulis, D., McGorry, P. D., Everall, I. P., & Foley, D. L. (2013). Effects of NRG1 and DAOA genetic variation on transition to psychosis in individuals at ultra-high risk for psychosis. Translational Psychiatry, 3, e251. https://doi.org/10.1038/tp.2013.23

    Article  Google Scholar 

  • Bouthour, W., Mégevand, P., Donoghue, J., Lüscher, C., Birbaumer, N., & Krack, P. (2019). Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nature Reviews Neurology, 15, 343–352. https://doi.org/10.1038/s41582-019-0166-4

    Article  Google Scholar 

  • Braithwaite, S. R., Giraud-Carrier, C., West, J., Barnes, M. D., & Hanson, C. L. (2016). Validating machine learning algorithms for twitter data against established measures of suicidality. JMIR Mental Health, 3, e4822. https://doi.org/10.2196/mental.4822

    Article  Google Scholar 

  • Bzdok, D., Altman, N., & Krzywinski, M. (2018). Statistics versus machine learning. Nature Methods, 15, 233–234. https://doi.org/10.1038/nmeth.4642

    Article  Google Scholar 

  • Cameron, G., Cameron, D. M., Megaw, G., Bond, R. B., Mulvenna, M., O’Neill, S. B., Armour, C., & McTear, M. (2017) Towards a chatbot for digital counselling. https://doi.org/10.14236/ewic/HCI2017.24

  • Charlson, F. J., Baxter, A. J., Cheng, H. G., Shidhaye, R., & Whiteford, H. A. (2016). The burden of mental, neurological, and substance use disorders in China and India: A systematic analysis of community representative epidemiological studies. The Lancet, 388, 376–389. https://doi.org/10.1016/S0140-6736(16)30590-6

    Article  Google Scholar 

  • Chen, S., He, Z., Han, X., He, X., Li, R., Zhu, H., Zhao, D., Dai, C., Zhang, Y., Lu, Z., Chi, X., & Niu, B. (2019). How big data and high-performance computing drive brain science. Genomics, Proteomics & Bioinformatics, Big Data in Brain Science, 17, 381–392. https://doi.org/10.1016/j.gpb.2019.09.003

    Article  Google Scholar 

  • Chilman, N., Song, X., Roberts, A., Tolani, E., Stewart, R., Chui, Z., Birnie, K., Harber-Aschan, L., Gazard, B., Chandran, D., Sanyal, J., Hatch, S., Kolliakou, A., & Das-Munshi, J. (2021). Text mining occupations from the mental health electronic health record: A natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK. British Medical Journal Open, 11, e042274. https://doi.org/10.1136/bmjopen-2020-042274

    Article  Google Scholar 

  • Clark, S. R., Schubert, K. O., & Baune, B. T. (2015). Towards indicated prevention of psychosis: Using probabilistic assessments of transition risk in psychosis prodrome. Journal of Neural Transmission, 122, 155–169. https://doi.org/10.1007/s00702-014-1325-9

    Article  Google Scholar 

  • Clementz, B. A., Parker, D. A., Trotti, R. L., McDowell, J. E., Keedy, S. K., Keshavan, M. S., Pearlson, G. D., Gershon, E. S., Ivleva, E. I., Huang, L.-Y., Hill, S. K., Sweeney, J. A., Thomas, O., Hudgens-Haney, M., Gibbons, R. D., & Tamminga, C. A. (2022). Psychosis biotypes: Replication and validation from the B-SNIP consortium. Schizophrenia Bulletin, 48, 56–68. https://doi.org/10.1093/schbul/sbab090

    Article  Google Scholar 

  • Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. AJP, 173, 373–384. https://doi.org/10.1176/appi.ajp.2015.14091200

    Article  Google Scholar 

  • Corcoran, C. M., & Cecchi, G. A. (2020). Using Language processing and speech analysis for the identification of psychosis and other disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Understanding the Nature and Treatment of Psychopathology: Letting the Data Guide the Way, 5, 770–779. https://doi.org/10.1016/j.bpsc.2020.06.004

    Article  Google Scholar 

  • De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65, 122–135. https://doi.org/10.1108/LR-06-2015-0061

    Article  Google Scholar 

  • Dinga, R., Schmaal, L., Penninx, B. W. J. H., van Tol, M. J., Veltman, D. J., van Velzen, L., Mennes, M., van der Wee, N. J. A., & Marquand, A. F. (2019). Evaluating the evidence for biotypes of depression: Methodological replication and extension of. Neuroimage Clinical, 22, 101796. https://doi.org/10.1016/j.nicl.2019.101796

    Article  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, 28–38. https://doi.org/10.1038/nm.4246

    Article  Google Scholar 

  • Du, J., Zhang, Y., Luo, J., Jia, Y., Wei, Q., Tao, C., & Xu, H. (2018). Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics and Decision Making, 18, 77–87. https://doi.org/10.1186/s12911-018-0632-8

    Article  Google Scholar 

  • Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115, 11203–11208. https://doi.org/10.1073/pnas.1802331115

    Article  Google Scholar 

  • Elvevåg, B., Foltz, P. W., Rosenstein, M., & DeLisi, L. E. (2010). An automated method to analyze language use in patients with schizophrenia and their first-degree relatives. J Neurolinguistics, 23, 270–284. https://doi.org/10.1016/j.jneuroling.2009.05.002

    Article  Google Scholar 

  • Frégnac, Y. (2017). Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain? Science, 358, 470–477. https://doi.org/10.1126/science.aan8866

    Article  Google Scholar 

  • Garg, K., Kumar, C. N., & Chandra, P. S. (2019). Number of psychiatrists in India: Baby steps forward, but a long way to go. Indian Journal of Psychiatry, 61, 104–105. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_7_18

    Article  Google Scholar 

  • GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9, 137–150. https://doi.org/10.1016/S2215-0366(21)00395-3

  • Grzenda, A., Kraguljac, N. V., McDonald, W. M., Nemeroff, C., Torous, J., Alpert, J. E., Rodriguez, C. I., & Widge, A. S. (2021). Evaluating the machine learning literature: A primer and user’s guide for psychiatrists. AJP, 178, 715–729. https://doi.org/10.1176/appi.ajp.2020.20030250

    Article  Google Scholar 

  • Harish, K. B., Price, W. N., & Aphinyanaphongs, Y. (2022). Open-source clinical machine learning models: Critical appraisal of feasibility, advantages, and challenges. JMIR Formative Research, 6, e33970. https://doi.org/10.2196/33970

    Article  Google Scholar 

  • Henson, P., Barnett, I., Keshavan, M., & Torous, J. (2020). Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophr, 6, 1–7. https://doi.org/10.1038/s41537-020-0100-1

  • Hu, K., Wang, M., Liu, Y., Yan, H., Song, M., Chen, J., Chen, Y., Wang, H., Guo, H., Wan, P., Lv, L., Yang, Y., Li, P., Lu, L., Yan, J., Wang, H., Zhang, H., Zhang, D., Wu, H., … Liu, B. (2021). Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score. Neuroimage Clinical, 32, 102860. https://doi.org/10.1016/j.nicl.2021.102860

    Article  Google Scholar 

  • Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010). Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, 748–751. https://doi.org/10.1176/appi.ajp.2010.09091379

    Article  Google Scholar 

  • Jiang, Q., Zhang, Y. & Pian, W. (2022). Chatbot as an emergency exist: Mediated empathy for resilience via human-AI interaction during the COVID-19 pandemic. Information Processing and Management, 103074. https://doi.org/10.1016/j.ipm.2022.103074

  • Karimian, G., Petelos, E., & Evers, S. M. A. A. (2022). The ethical issues of the application of artificial intelligence in healthcare: A systematic scoping review. AI Ethics. https://doi.org/10.1007/s43681-021-00131-7

    Article  Google Scholar 

  • Kent, P., Cancelliere, C., Boyle, E., Cassidy, J. D., & Kongsted, A. (2020). A conceptual framework for prognostic research. BMC Medical Research Methodology, 20, 172. https://doi.org/10.1186/s12874-020-01050-7

    Article  Google Scholar 

  • Kisling, L. A., & Das, J. M. (2022). Prevention strategies, StatPearls [internet]. StatPearls Publishing.

    Google Scholar 

  • Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021). Deep learning for small and big data in psychiatry. Neuropsychopharmacology, 46, 176–190. https://doi.org/10.1038/s41386-020-0767-z

    Article  Google Scholar 

  • Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., Brown, T. A., Carpenter, W. T., Caspi, A., Clark, L. A., Eaton, N. R., Forbes, M. K., Forbush, K. T., Goldberg, D., Hasin, D., Hyman, S. E., Ivanova, M. Y., Lynam, D. R., Markon, K., … Zimmerman, M. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. https://doi.org/10.1037/abn0000258

  • Koutsouleris, N., Borgwardt, S., Meisenzahl, E. M., Bottlender, R., Möller, H.-J., & Riecher-Rössler, A. (2012). Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: Results from the FePsy study. Schizophrenia Bulletin, 38, 1234–1246. https://doi.org/10.1093/schbul/sbr145

    Article  Google Scholar 

  • Kraguljac, N. V., McDonald, W. M., Widge, A. S., Rodriguez, C. I., Tohen, M., & Nemeroff, C. B. (2021). Neuroimaging biomarkers in schizophrenia. AJP, appi.ajp.2020.2. https://doi.org/10.1176/appi.ajp.2020.20030340

  • Leenings, R., Winter, N. R., Dannlowski, U., & Hahn, T. (2022). Recommendations for machine learning benchmarks in neuroimaging. NeuroImage, 257, 119298. https://doi.org/10.1016/j.neuroimage.2022.119298

    Article  Google Scholar 

  • Liu, Y., Xia, S., Nie, J., Wei, P., Shu, Z., Chang, J. A., & Jiang, X. (2022). aiMSE: Toward an AI-based online mental status examination. IEEE Pervasive Computing, 1–9. https://doi.org/10.1109/MPRV.2022.3172419

  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (pp. 4768–4777). Curran Associates Inc., Red Hook.

    Google Scholar 

  • Marcinkevičs, R., & Vogt, J. E. (2023). Interpretable and explainable machine learning: A methods-centric overview with concrete examples. Wires Data Mining and Knowledge Discovery, 13, e1493. https://doi.org/10.1002/widm.1493

    Article  Google Scholar 

  • Michalowski, M., Abidi, S. S. R., & Abidi, S. (2022). Artificial Intelligence in Medicine.

    Google Scholar 

  • McCoy, T. H., Castro, V. M., Hart, K. L., Pellegrini, A. M., Yu, S., Cai, T., & Perlis, R. H. (2018). Genome-wide association study of dimensional psychopathology using electronic health records. Biological Psychiatry, Impulsivity: Mechanisms and Manifestations, 83, 1005–1011. https://doi.org/10.1016/j.biopsych.2017.12.004

    Article  Google Scholar 

  • McCoy, T. H., Jr., Castro, V. M., Roberson, A. M., Snapper, L. A., & Perlis, R. H. (2016). Improving prediction of suicide and accidental death after discharge from general hospitals with natural language processing. JAMA Psychiatry, 73, 1064–1071. https://doi.org/10.1001/jamapsychiatry.2016.2172

    Article  Google Scholar 

  • Meskó, B., & Görög, M. (2020). A short guide for medical professionals in the era of artificial intelligence. Npj Digital Medicine, 3, 1–8. https://doi.org/10.1038/s41746-020-00333-z

    Article  Google Scholar 

  • Mota, N. B., Vasconcelos, N. A. P., Lemos, N., Pieretti, A. C., Kinouchi, O., Cecchi, G. A., Copelli, M., & Ribeiro, S. (2012). Speech graphs provide a quantitative measure of thought disorder in psychosis. PLoS ONE, 7, e34928. https://doi.org/10.1371/journal.pone.0034928

    Article  Google Scholar 

  • Myin-Germeys, I., Kasanova, Z., Vaessen, T., Vachon, H., Kirtley, O., Viechtbauer, W., & Reininghaus, U. (2018). Experience sampling methodology in mental health research: New insights and technical developments. World Psychiatry, 17, 123–132. https://doi.org/10.1002/wps.20513

    Article  Google Scholar 

  • Nikolac Perkovic, M., Nedic Erjavec, G., Svob Strac, D., Uzun, S., Kozumplik, O., & Pivac, N. (2017). Theranostic biomarkers for schizophrenia. International Journal of Molecular Sciences, 18, 733. https://doi.org/10.3390/ijms18040733

    Article  Google Scholar 

  • Noble, J. M., Zamani, A., Gharaat, M., Merrick, D., Maeda, N., Lambe Foster, A., Nikolaidis, I., Goud, R., Stroulia, E., Agyapong, V. I. O., Greenshaw, A. J., Lambert, S., Gallson, D., Porter, K. T., Turner, D., & Zaïane, O. R. (2022). Developing, implementing, and evaluating an artificial intelligence-guided mental health resource navigation chatbot for health care workers and their families during and following the COVID-19 pandemic: protocol for a cross-sectional study. JMIR Research Protocols. https://doi.org/10.2196/33717

    Article  Google Scholar 

  • Palaniyappan, L., Mota, N. B., Oowise, S., Balain, V., Copelli, M., Ribeiro, S., & Liddle, P. F. (2019). Speech structure links the neural and socio-behavioural correlates of psychotic disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 88, 112–120. https://doi.org/10.1016/j.pnpbp.2018.07.007

    Article  Google Scholar 

  • Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019). Introducing artificial intelligence training in medical education. JMIR Medical Education, 5, e16048. https://doi.org/10.2196/16048

    Article  Google Scholar 

  • Patel, V., Xiao, S., Chen, H., Hanna, F., Jotheeswaran, A. T., Luo, D., Parikh, R., Sharma, E., Usmani, S., Yu, Y., Druss, B. G., & Saxena, S. (2016). The magnitude of and health system responses to the mental health treatment gap in adults in India and China. The Lancet, 388, 3074–3084. https://doi.org/10.1016/S0140-6736(16)00160-4

    Article  Google Scholar 

  • Paus, T. (2010). Population neuroscience: Why and how. Human Brain Mapping, 31, 891–903. https://doi.org/10.1002/hbm.21069

    Article  Google Scholar 

  • Pennachin, C., & Goertzel, B. (2007). Contemporary approaches to artificial general intelligence. In Goertzel, B., Pennachin, C. (eds.), Artificial general intelligence, cognitive technologies (pp. 1–30). Springer. https://doi.org/10.1007/978-3-540-68677-4_1

  • Posoldova, A. (2020). Machine learning pipelines: from research to production. IEEE Potentials, 39, 38–42. https://doi.org/10.1109/MPOT.2020.3016280

    Article  Google Scholar 

  • Pradeep, B. S., Gururaj, G., Varghese, M., Benegal, V., Rao, G. N., Sukumar, G. M., Amudhan, S., Arvind, B., Girimaji, S., K., T., P., M., Vijayasagar, K.J., Bhaskarapillai, B., Thirthalli, J., Loganathan, S., Kumar, N., Sudhir, P., et al. (2018). National Mental Health Survey of India, 2016—Rationale, design and methods. PLoS One, 13, e0205096. https://doi.org/10.1371/journal.pone.0205096

  • Raket, L. L., Jaskolowski, J., Kinon, B. J., Brasen, J. C., Jönsson, L., Wehnert, A., & Fusar-Poli, P. (2020). Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: A case-control development and validation study. The Lancet Digital Health, 2, e229–e239. https://doi.org/10.1016/S2589-7500(20)30024-8

    Article  Google Scholar 

  • Rashid, B., & Calhoun, V. (2020). Towards a brain-based predictome of mental illness. Human Brain Mapping, 41, 3468–3535. https://doi.org/10.1002/hbm.25013

    Article  Google Scholar 

  • Ray, A., Bhardwaj, A., Malik, Y. K., Singh, S., & Gupta, R. (2022). Artificial intelligence and psychiatry: An overview. Asian Journal of Psychiatry, 70, 103021. https://doi.org/10.1016/j.ajp.2022.103021

    Article  Google Scholar 

  • Reece, A. G., Reagan, A. J., Lix, K. L. M., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Science and Reports, 7, 13006. https://doi.org/10.1038/s41598-017-12961-9

    Article  Google Scholar 

  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. presented at the KDD ’16: The 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778

  • Riecher-Rössler, A., Pflueger, M. O., Aston, J., Borgwardt, S. J., Brewer, W. J., Gschwandtner, U., & Stieglitz, R.-D. (2009). Efficacy of using cognitive status in predicting psychosis: A 7-year follow-up. Biological Psychiatry, 66, 1023–1030. https://doi.org/10.1016/j.biopsych.2009.07.020

    Article  Google Scholar 

  • Robins, E., & Guze, S. B. (1970). Establishment of diagnostic validity in psychiatric illness: Its application to schizophrenia. American Journal of Psychiatry, 126, 983–987. https://doi.org/10.1176/ajp.126.7.983

    Article  Google Scholar 

  • Rodriguez-Villa, E., Mehta, U. M., Naslund, J., Tugnawat, D., Gupta, S., Thirtalli, J., Bhan, A., Patel, V., Chand, P. K., Rozatkar, A., Keshavan, M., & Torous, J. (2021). Smartphone health assessment for relapse prevention (SHARP): A digital solution toward global mental health. Bjpsych Open, 7, e29. https://doi.org/10.1192/bjo.2020.142

    Article  Google Scholar 

  • Rosen, M., Betz, L. T., Schultze-Lutter, F., Chisholm, K., Haidl, T. K., Kambeitz-Ilankovic, L., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Ruhrmann, S., Salokangas, R. K. R., Upthegrove, R., Wood, S. J., Koutsouleris, N., & Kambeitz, J. (2021). Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample. Neuroscience & Biobehavioral Reviews, 125, 478–492. https://doi.org/10.1016/j.neubiorev.2021.02.032

    Article  Google Scholar 

  • Salazar de Pablo, G., Studerus, E., Vaquerizo-Serrano, J., Irving, J., Catalan, A., Oliver, D., Baldwin, H., Danese, A., Fazel, S., Steyerberg, E.W., Stahl, D., & Fusar-Poli, P. (2020). Implementing precision psychiatry: A systematic review of individualized prediction models for clinical practice. Schizophrenia Bulletin, sbaa120. https://doi.org/10.1093/schbul/sbaa120

  • Scangos, K. W., Khambhati, A. N., Daly, P. M., Makhoul, G. S., Sugrue, L. P., Zamanian, H., Liu, T. X., Rao, V. R., Sellers, K. K., Dawes, H. E., Starr, P. A., Krystal, A. D., & Chang, E. F. (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression. Nature Medicine, 27, 1696–1700. https://doi.org/10.1038/s41591-021-01480-w

    Article  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, 1426–1448. https://doi.org/10.1017/S0033291719000151

    Article  Google Scholar 

  • Singh, O. P. (2019). Chatbots in psychiatry: Can treatment gap be lessened for psychiatric disorders in India. Indian Journal of Psychiatry. https://doi.org/10.4103/0019-5545.258323

    Article  Google Scholar 

  • Solanes, A., & Radua, J. (2022). Advances in using MRI to estimate the risk of future outcomes in mental health—Are we getting there? Front Psychiatry, 13, fpsyt-13-826111. https://doi.org/10.3389/fpsyt.2022.826111

  • Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O. (2023). Multimodal data fusion for cancer biomarker discovery with deep learning. Nature Machine Intelligence, 5, 351–362. https://doi.org/10.1038/s42256-023-00633-5

    Article  Google Scholar 

  • Turing, A. M. (1950). I.—Computing machinery and intelligence. Mind LIX, 433–460. https://doi.org/10.1093/mind/LIX.236.433

  • Uhlhaas, P. J., & Wood, S. J. (Eds.) (2020). Biological, psychological and sociocultural processes in emerging mental disorders in youth. In Youth mental health: A paradigm for prevention and early intervention. The MIT Press. https://doi.org/10.7551/mitpress/13412.003.0010

  • 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, 456–464. https://doi.org/10.1177/0706743719828977

    Article  Google Scholar 

  • Van Tricht, M. J., Nieman, D. H., Koelman, J. H. T. M., van der Meer, J. N., Bour, L. J., de Haan, L., & Linszen, D. H. (2010). Reduced parietal P300 amplitude is associated with an increased risk for a first psychotic episode. Biological Psychiatry, 68, 642–648. https://doi.org/10.1016/j.biopsych.2010.04.022

    Article  Google Scholar 

  • Wang, L., & Miller, L. C. (2020). Just-in-the-moment adaptive interventions (JITAI): A meta-analytical review. Health Communication, 35, 1531–1544. https://doi.org/10.1080/10410236.2019.1652388

    Article  Google Scholar 

  • Watson, D. S. (2022). Conceptual challenges for interpretable machine learning. Synthese, 200, 65. https://doi.org/10.1007/s11229-022-03485-5

    Article  Google Scholar 

  • Wiese, W., & Friston, K. J. (2022). AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness. Behavioural Brain Research, 420, 113704. https://doi.org/10.1016/j.bbr.2021.113704

    Article  Google Scholar 

  • World Health Organization. (2022). World mental health report: Transforming mental health for all. World Health Organization.

    Google Scholar 

  • World Health Organization. (1992). Tenth revision of the international classification of diseases and related health problems.

    Google Scholar 

  • Xie, Y., Ding, H., Du, X., Chai, C., Wei, X., Sun, J., Zhuo, C., Wang, L., Li, J., Tian, H., Liang, M., Zhang, S., Yu, C., & Qin, W. (2022). Morphometric integrated classification index: a multisite model-based, interpretable, shareable and evolvable biomarker for schizophrenia. Schizophr Bulletin, sbac096. https://doi.org/10.1093/schbul/sbac096

  • Yang, Q., Li, Y., Li, B., & Gong, Y. (2022). A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data. Computers in Biology and Medicine, 148, 105956. https://doi.org/10.1016/j.compbiomed.2022.105956

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India

    Urvakhsh Meherwan Mehta & Kiran Basawaraj Bagali

  2. Machine Learning, Tavant Technologies, Santa Clara, CA, USA

    Sriharshasai Kommanapalli

Authors
  1. Urvakhsh Meherwan Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiran Basawaraj Bagali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sriharshasai Kommanapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Urvakhsh Meherwan Mehta .

Editor information

Editors and Affiliations

  1. Dean, School of Humanities & Head, Consciousness Studies Programme National Institute of Advanced Studies, Indian Institute of Science Bangalore, Bangalore, India

    Sangeetha Menon

  2. Human Sciences Research Group, International Institute of Information Technology, Hyderabad, India

    Saurabh Todariya

  3. Adjunct Professor, National Institute of Advanced Studies, Indian Institute of Science Bangalore, Bangalore, India

    Tilak Agerwala

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mehta, U.M., Bagali, K.B., Kommanapalli, S. (2024). Mind-Reading Machines: Promises, Pitfalls, and Solutions of Implementing Machine Learning in Mental Health. In: Menon, S., Todariya, S., Agerwala, T. (eds) AI, Consciousness and The New Humanism. Springer, Singapore. https://doi.org/10.1007/978-981-97-0503-0_10

Download citation

Publish with us

Policies and ethics

Search

Navigation