Machine Learning Approaches for Clinical Psychology and Psychiatry
Abstract
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multimodal and intensive sampling data streams might be particularly promising inputs for machine learning models because these models can often handle many correlated predictors.
“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [135]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280 · see citing works
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505 · see citing works
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., et al. (2006) · American Journal of Psychiatry · doi:10.1176/ajp.2006.163.11.1905 · Cited by 5,643 · see citing works
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359 · see citing works
The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses
Hofmann, S. G., Asnaani, A., Vonk, I. J. J., Sawyer, A. T., & Fang, A. (2012) · Cognitive Therapy and Research · doi:10.1007/s10608-012-9476-1 · Cited by 3,445 · see citing works
New Effect Size Rules of Thumb
Sawilowsky, S. S. (2009) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1257035100 · Cited by 3,254 · see citing works
The New Statistics: Why and How
Cumming, G. (2014) · Psychological Science · doi:10.1177/0956797613504966 · Cited by 3,200 · see citing works
Raise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979 · see citing works
Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485 · see citing works
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · see citing works
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
What does research reproducibility mean?
Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf5027 · Cited by 1,293 · see citing works
Insel, T. R., & Cuthbert, B. N. (2015) · Science · doi:10.1126/science.aab2358 · Cited by 840 · see citing works
Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers
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) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14091200 · Cited by 768 · see citing works
Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676 · see citing works
Why Most Clinical Research Is Not Useful
Ioannidis, J. P. A. (2016) · PLOS Medicine · doi:10.1371/journal.pmed.1002049 · Cited by 655 · see citing works
An Individualized Risk Calculator for Research in Prodromal Psychosis
Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Heinssen, R., Jeffries, C. D., Mathalon, D. H., McGlashan, T. H., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15070890 · Cited by 574 · see citing works
Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1115365109 · Cited by 464 · see citing works
van der Gaag, M., Smit, F., Bechdolf, A., French, P., Linszen, D. H., Yung, A. R., McGorry, P., & Cuijpers, P. (2013) · Schizophrenia Research · doi:10.1016/j.schres.2013.07.004 · Cited by 384 · see citing works
The search for imaging biomarkers in psychiatric disorders
Abi-Dargham, A., & Horga, G. (2016) · Nature Medicine · doi:10.1038/nm.4190 · Cited by 311 · see citing works
Brain connectomics predict response to treatment in social anxiety disorder
Whitfield-Gabrieli, S., Ghosh, S. S., Nieto-Castanon, A., Saygin, Z., Doehrmann, O., Chai, X. J., Reynolds, G. O., Hofmann, S. G., Pollack, M. H., & Gabrieli, J. D. E. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.109 · Cited by 210 · see citing works
When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging
Whelan, R., & Garavan, H. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.05.014 · Cited by 209 · see citing works
Stratified medicine for mental disorders
Schumann, G., Binder, E. B., Holte, A., de Kloet, E. R., Oedegaard, K. J., Robbins, T. W., Walker-Tilley, T. R., Bitter, I., Brown, V. J., Buitelaar, J., et al. (2014) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2013.09.010 · Cited by 186 · see citing works
Carrión, R. E., Cornblatt, B. A., Burton, C. Z., Tso, I. F., Auther, A. M., Adelsheim, S., Calkins, R., Carter, C. S., Niendam, T., Sale, T. G., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15121565 · Cited by 170 · see citing works
Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., & Venkatesh, S. (2014) · BMC Psychiatry · doi:10.1186/1471-244x-14-76 · Cited by 150 · see citing works
Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy
Askland, K. D., Garnaat, S., Sibrava, N. J., Boisseau, C. L., Strong, D., Mancebo, M., Greenberg, B., Rasmussen, S., & Eisen, J. (2015) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1463 · Cited by 75 · see citing works
Van Schependom, J., Yu, W., Gielen, J., Laton, J., De Keyser, J., De Hert, M., & Nagels, G. (2015) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.14m09367 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631 · see citing works
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429 · see citing works
Deep learning in mental health outcome research: a scoping review
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368 · see citing works
Stein, D. J., Shoptaw, S. J., Vigo, D. V., Lund, C., Cuijpers, P., Bantjes, J., Sartorius, N., & Maj, M. (2022) · World Psychiatry · doi:10.1002/wps.20998 · Cited by 331 · see citing works
Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3604 · Cited by 275 · see citing works
Self-citationFlesia, L., Monaro, M., Mazza, C., Fietta, V., Colicino, E., Segatto, B., & Roma, P. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9103350 · Cited by 291 · see citing works
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221134490 · Cited by 263 · see citing works
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Self-citationDeep learning for small and big data in psychiatry
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189 · see citing works
The Science of Prognosis in Psychiatry: A Review
Fusar-Poli, P., Hijazi, Z., Stahl, D., & Steyerberg, E. W. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2530 · Cited by 197 · see citing works
Bickman, L. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01065-8 · Cited by 184 · see citing works
A Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179 · see citing works
TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169 · see citing works
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022) · Psychology Learning & Teaching · doi:10.1177/14757257211037149 · Cited by 153 · see citing works
Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.009 · Cited by 82 · see citing works
Self-citationA common factors perspective on mindfulness-based interventions
Goldberg, S. B. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00090-8 · Cited by 75 · see citing works
Kleiman, E. M., Glenn, C. R., & Liu, R. T. (2019) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2019.1666400 · Cited by 68 · see citing works
Zhang, M., Scandiffio, J., Younus, S., Jeyakumar, T., Karsan, I., Charow, R., Salhia, M., & Wiljer, D. (2023) · JMIR Formative Research · doi:10.2196/47847 · Cited by 67 · see citing works
“When considering the prognosis of mental health issues, using longitudinal data and algorithms can improve the ability of AI to strengthen predictions for patients in psychiatry [1,3].”
BackgroundMethodKearney, C. A. (2021) · Child & Youth Care Forum · doi:10.1007/s10566-020-09591-0 · Cited by 51 · see citing works
Lalousis, P. A., Wood, S. J., Schmaal, L., Chisholm, K., Griffiths, S. L., Reniers, R. L. E. P., Bertolino, A., Borgwardt, S., Brambilla, P., Kambeitz, J., et al. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa185 · Cited by 42 · see citing works
Self-citationPredicting early dropout in online versus face-to-face guided self-help: A machine learning approach
Gonzalez Salas Duhne, P., Delgadillo, J., & Lutz, W. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104200 · Cited by 38 · see citing works
Is it ethical to use prognostic estimates from machine learning to treat psychosis
(2018) · AMA Journal of Ethics · doi:10.1001/amajethics.2018.804 · Cited by 33 · see citing works
What the future holds: Machine learning to predict success in psychotherapy
Taubitz, F.-S., Büdenbender, B., & Alpers, G. W. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104116 · Cited by 25 · see citing works
Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Wiles, N., Kessler, D., Gilbody, S., Hollon, S. D., Kendrick, T., Watkins, E., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11121295 · Cited by 21 · see citing works
“This is often not possible with regression based or supervised machine learning models because the reasons for any individual being classified as having a particular degree of risk for the outcome of interest may be hard to determine and often harder still to explain when there are multiple predictor variables [46,47].”
BackgroundPairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters
Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D. (2020) · doi:10.31234/osf.io/2yber · Cited by 12 · see citing works
Jiwani, Z., Goldberg, S. B., Stroud, J., Young, J., Curtin, J., Dunne, J. D., Simonsson, O., Webb, C. A., Carhart-Harris, R., & Schlosser, M. (2025) · PLOS ONE · doi:10.1371/journal.pone.0310160 · Cited by 6 · see citing works
- Authors
- Dwyer, D. B., Falkai, P., & Koutsouleris, N.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Venue
- Annual Review of Clinical Psychology
- Publisher
- Annual Reviews
- Published
- 2018-05-07
- Volume
- 14
- Issue
- 1
- Pages
- 91-118
- ISSN
- 1548-5943 (ISSN-L)
- 1548-5951
- OpenAlex
- W2787427645
- Cited by
- 966
- References
- 34
- Citations by year
- 2026: 124 · 2025: 172 · 2024: 142 · 2023: 103 · 2022: 135 · 2021: 118 · 2020: 113 · 2019: 46 · 2018: 13
- FWCI
- 13.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Clinical Psychology (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- machine learning, clinical psychology, psychiatry, mental illness, diagnosis, prognosis, treatment prediction, clinical data, biological data, statistical limitations, clinical translation, prediction model
- MeSH terms
- Machine Learning, Mental Disorders, Humans, Psychiatry, Psychology, Clinical, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
