Cross-trial prediction of treatment outcome in depression: a machine learning approach
Abstract
Background Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.Methods We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).Findings We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.Interpretation Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.Funding Yale University.
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Machine learning algorithms, based on both clinical information and biomarkers, are increasingly capable of predicting treatment outcome.
“Machine learning algorithms, based on both clinical information and biomarkers, are increasingly capable of predicting treatment outcome.58”
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Draper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993 · see citing works
Building Predictive Models in R Using the caret Package
Kühn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 9,986 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788 · see citing works
The Elements of Statistical Learning
Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009) · Springer series in statistics · doi:10.1007/b94608 · Cited by 4,111 · see citing works
Trivedi, M. H., Rush, A. J., Wisniewski, S. R., Nierenberg, A. A., Warden, D., Ritz, L., Norquist, G., Howland, R. H., Lebowitz, B., McGrath, P. J., et al. (2006) · American Journal of Psychiatry · doi:10.1176/appi.ajp.163.1.28 · Cited by 3,879 · see citing works
Self-citationAPACHE—acute physiology and chronic health evaluation: a physiologically based classification system
Knaus, W. A., Zimmerman, J. E., Wagner, D. P., Draper, E. A., & Lawrence, D. E. (1981) · Critical Care Medicine · doi:10.1097/00003246-198108000-00008 · Cited by 2,190 · see citing works
Cipriani, A., Furukawa, T. A., Salanti, G., Geddes, J. R., Higgins, J. P., Churchill, R., Watanabe, N., Nakagawa, A., Omori, I. M., McGuire, H., et al. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60046-5 · Cited by 1,654 · see citing works
Diagnostic exome sequencing in persons with severe intellectual disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Vulto-van Silfhout, A. T., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1206524 · Cited by 1,546 · see citing works
Fried, E. I., & Nesse, R. M. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.10.010 · Cited by 1,043 · see citing works
Insel, T. R., & Cuthbert, B. N. (2015) · Science · doi:10.1126/science.aab2358 · Cited by 840 · see citing works
The STAR*D project results: A comprehensive review of findings
Warden, D., Rush, A. J., Trivedi, M. H., Fava, M., & Wisniewski, S. R. (2007) · Current Psychiatry Reports · doi:10.1007/s11920-007-0061-3 · Cited by 727 · see citing works
Self-citationCombrisson, E., & Jerbi, K. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.01.010 · Cited by 612 · see citing works
DeRubeis, R. J., Cohen, Z. D., Forand, N. R., Fournier, J. C., Gelfand, L. A., & Lorenzo-Luaces, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0083875 · Cited by 509 · see citing works
Wijeysundera, D. N., Karkouti, K., Dupuis, J., Rao, V., Chan, C. T., Granton, J. T., & Beattie, W. S. (2007) · JAMA · doi:10.1001/jama.297.16.1801 · Cited by 471 · see citing works
Regression Analysis: A Constructive Critique
Berk, R. A. (2004) · Sage eBooks · doi:10.4135/9781483348834 · Cited by 466 · see citing works
Kessler, R. C., Warner, C. H., Ivany, C., Petukhova, M. V., Rose, S., Bromet, E. J., Brown, M., Cai, T., Colpe, L. J., Cox, K. L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1754 · Cited by 447 · see citing works
Uher, R., Perlis, R. H., Henigsberg, N., Zobel, A., Rietschel, M., Mors, O., Hauser, J., Dernovsek, M. Z., Souery, D., Bajs, M., et al. (2012) · Psychological Medicine · doi:10.1017/s0033291711001905 · Cited by 420 · see citing works
Zimmerman, M., & Mattia, J. I. (2001) · Archives of General Psychiatry · doi:10.1001/archpsyc.58.8.787 · Cited by 375 · see citing works
Rush, A. J., Trivedi, M. H., Stewart, J. W., Nierenberg, A. A., D, M. F. M., Kurian, B. T., Warden, D., Morris, D. W., Luther, J. F., Husain, M. M., et al. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.10111645 · Cited by 372 · see citing works
Self-citationPrognostic Nomogram for Patients Undergoing Resection for Adenocarcinoma of the Pancreas
Brennan, M. F., Kattan, M. W., Klimstra, D. S., & Conlon, K. C. (2004) · Annals of Surgery · doi:10.1097/01.sla.0000133125.85489.07 · Cited by 367 · see citing works
Comparisons of nomograms and urologists' predictions in prostate cancer.
Ross, P., Gerigk, C., Gonen, M., Yossepowitch, O., Cagiannos, I., Sogani, P., Scardino, P., & Kattan, M. (2002) · Seminars in Urologic Oncology · doi:10.1053/suro.2002.32490 · Cited by 309 · see citing works
Specht, M., Kattan, M., Gonen, M., Fey, J., & Zee, K. V. (2005) · Annals of Surgical Oncology · doi:10.1245/aso.2005.06.037 · Cited by 290 · see citing works
Phillips, M. L., Chase, H. W., Sheline, Y. I., Etkin, A., Almeida, J. R. C., Deckersbach, T., & Trivedi, M. H. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14010076 · Cited by 267 · see citing works
Self-citationFluoxetine versus other types of pharmacotherapy for depression
Magni, L. R., Purgato, M., Gastaldon, C., Papola, D., Furukawa, T. A., Cipriani, A., & Barbui, C. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004185.pub3 · Cited by 251 · see citing works
GENDEP Investigators, MARS Investigators, STAR*D Investigators, Uher, R., Tansey, K. E., Henigsberg, N., Wolfgang, M. C., Mors, O. N., Hauser, J., Placentino, A., et al. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12020237 · Cited by 241 · see citing works
Diagnostic Exome Sequencing in Persons With Severe Intellectual Disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Silfhout, A. T. V., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2013) · Obstetrical & Gynecological Survey · doi:10.1097/01.ogx.0000428160.59063.a6 · Cited by 204 · see citing works
Pfeiffer, R. M., Park, Y., Kreimer, A. R., Lacey, J. V., Pee, D., Greenlee, R. T., Buys, S. S., Hollenbeck, A. R., ROSNER, B. A., Gail, M. H., et al. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001492 · Cited by 189 · see citing works
Managing depression as a chronic disease: a randomised trial of ongoing treatment in primary care
ROST, K. M. (2002) · BMJ · doi:10.1136/bmj.325.7370.934 · Cited by 178 · see citing works
Selecting Among Second-Step Antidepressant Medication Monotherapies
Rush, A. J., Wisniewski, S. R., Warden, D., Luther, J. F., Davis, L. L., D, M. F. M., Nierenberg, A. A., & Trivedi, M. H. (2008) · Archives of General Psychiatry · doi:10.1001/archpsyc.65.8.870 · Cited by 177 · see citing works
Self-citationLeuchter, A. F., Cook, I. A., Marangell, L. B., Gilmer, W. S., Burgoyne, K. S., Howland, R. H., Trivedi, M. H., Zisook, S., Jain, R., McCracken, J. T., et al. (2009) · Psychiatry Research · doi:10.1016/j.psychres.2009.06.004 · Cited by 177 · see citing works
Self-citationAssessing Individual Risk for Prostate Cancer
Nam, R. K., Toi, A., Klotz, L. H., Trachtenberg, J., Jewett, M. A. S., Appu, S., Loblaw, D. A., Sugar, L., Narod, S. A., & Kattan, M. W. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2007.10.6450 · Cited by 175 · see citing works
A Clinical Risk Stratification Tool for Predicting Treatment Resistance in Major Depressive Disorder
Perlis, R. H. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.12.007 · Cited by 170 · see citing works
Biomarkers to Predict Antidepressant Response
Leuchter, A. F., Cook, I. A., Hamilton, S. P., Narr, K. L., Toga, A. W., Hunter, A. M., Faull, K. F., Whitelegge, J. P., Andrews, A. M., Loo, J., et al. (2010) · Current Psychiatry Reports · doi:10.1007/s11920-010-0160-4 · Cited by 140 · see citing works
Recent Advances in Predictive (Machine) Learning
Friedman, J. H. (2006) · Journal of Classification · doi:10.1007/s00357-006-0012-4 · Cited by 133 · see citing works
Fluoxetine versus other types of pharmacotherapy for depression
Cipriani, A., Brambilla, P., Furukawa, T. A., Geddes, J. R., Gregis, M., Hotopf, M. H., Malvini, L., & Barbui, C. (2005) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004185.pub2 · Cited by 130 · see citing works
Pragmatism Instead of Mechanism
Paulus, M. P. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0497 · Cited by 67 · see citing works
What if a placebo effect explained all the activity of depression treatments?
Cuijpers, P., & Cristea, I. A. (2015) · World Psychiatry · doi:10.1002/wps.20249 · Cited by 61 · see citing works
Kattan, M. W., Yu, C., Stephenson, A. J., Sartor, O. A., & TOMBAL, B. F. (2013) · Urology · doi:10.1016/j.urology.2012.12.010 · Cited by 53 · see citing works
Tiong, H. Y., Goldfarb, D. A., Kattan, M. W., Alster, J. M., Thuita, L. W., Cheng, Y., Wee, A., & Poggio, E. D. (2009) · The Journal of Urology · doi:10.1016/j.juro.2008.10.164 · Cited by 46 · see citing works
Cues they use: clinicians' endorsement of risk cues in predictions of dangerousness
Odeh, M. S., Zeiss, R. A., & Huss, M. T. (2006) · Behavioral Sciences & the Law · doi:10.1002/bsl.672 · Cited by 28 · see citing works
Elemental Spatial and Temporal Association Formation in Left Temporal Lobe Epilepsy
Benjamin, C. F. A., Saling, M. M., WOOD, A. G., & Reutens, D. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100891 · Cited by 26 · see citing works
Personalised pharmacotherapy: an interim solution for antidepressant treatment?
Chekroud, A. M., & Krystal, J. H. (2015) · BMJ · doi:10.1136/bmj.h2502 · Cited by 16 · see citing works
Self-citationKumbhani, D. J., Wells, B. J., Lincoff, A. M., Jain, A., Arrigain, S., Yu, C., Goormastic, M., Ellis, S. G., Blackstone, E. H., & Kattan, M. W. (2013) · Europe PMC (PubMed Central) · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“Another example of an incorrect prediction by ML that could be fixed by interpretability is the case of a deep learning model that was developed to predict which patients would benefit from an antidepressant medication called escitalopram [35].”
BackgroundComputational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
Establishment of Best Practices for Evidence for Prediction: A Review
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3671 · Cited by 738 · see citing works
The 52 symptoms of major depression: lack of content overlap among seven common depression scales
Fried, E. I. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.10.019 · Cited by 649 · see citing works
The promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531 · see citing works
Self-citationAutomated assessment of psychiatric disorders using speech: A systematic review
Low, D. M., Bentley, K. H., & Ghosh, S. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.354 · Cited by 519 · see citing works
The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443 · see citing works
Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 477 · see citing works
Self-citationTreatment selection in depression.
Cohen, Z. D., & DeRubeis, R. J. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084746 · Cited by 435 · see citing works
Maj, M., Stein, D. J., Parker, G., Zimmerman, M., Fava, G. A., De Hert, M., Demyttenaere, K., McIntyre, R. S., Widiger, T., & Wittchen, H. (2020) · World Psychiatry · doi:10.1002/wps.20771 · Cited by 435 · 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
Artificial intelligence and the implementation challenge
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13659 · Cited by 389 · see citing works
AI in Health: State of the Art, Challenges, and Future Directions
Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398 · see citing works
Lee, Y. L., Ragguett, R., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A. P., Brietzke, E. M., Lin, K., Pan, Z., Subramaniapillai, M., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.073 · Cited by 411 · see citing works
The WPA-Lancet psychiatry commission on the future of psychiatry
Bhugra, D., Tasman, A., Pathare, S., Priebe, S., Smith, S., Torous, J., Arbuckle, M. R., Langford, A., Alarcón, R. D., Chiu, H. F. K., et al. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30333-4 · Cited by 392 · see citing works
Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2165 · Cited by 341 · see citing works
Chekroud, A. M., Gueorguieva, R., Krumholz, H. M., Trivedi, M. H., Krystal, J. H., & McCarthy, G. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0025 · Cited by 276 · see citing works
Self-citationFrom 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
Illusory generalizability of clinical prediction models
Chekroud, A. M., Hawrilenko, M., Loho, H., Bondar, J., Gueorguieva, R., Hasan, A., Kambeitz, J., Corlett, P. R., Koutsouleris, N., Krumholz, H. M., et al. (2024) · Science · doi:10.1126/science.adg8538 · Cited by 251 · see citing works
Self-citationJacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247 · see citing works
Moving forward: how depression heterogeneity hinders progress in treatment and research
Fried, E. (2017) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2017.1307737 · Cited by 220 · see citing works
Internet-Based Cognitive-Behavioral Therapy for Depression: Current Progress and Future Directions
Webb, C. A., Rosso, I. M., & Rauch, S. L. (2017) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000139 · Cited by 217 · see citing works
Review of Machine Learning Algorithms for Diagnosing Mental Illness
Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019) · Psychiatry Investigation · doi:10.30773/pi.2018.12.21.2 · Cited by 237 · see citing works
Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges
Chung, J., & Teo, J. (2022) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2022/9970363 · Cited by 223 · see citing works
de Pablo, G. S., Studerus, E., Vaquerizo‐Serrano, J., Irving, J., Catalán, A., Oliver, D., Baldwin, H., Danese, A., Fazel, S., Steyerberg, E. W., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa120 · Cited by 221 · see citing works
Koutsouleris, N., Kahn, R. S., Chekroud, A. M., Leucht, S., Falkai, P., Wobrock, T., Derks, E. M., Fleischhacker, W., & Hasan, A. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30171-7 · Cited by 217 · see citing works
Self-citationGonzález, P. C., He, A., Lam, E. K. M., Ng, I. M. C., Li, M. W., Hou, R., Chan, J. N.-M., Sahni, Y., Viñas‐Guasch, N., Miller, T., et al. (2025) · Psychological Medicine · doi:10.1017/s0033291724003295 · Cited by 252 · 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
Machine learning and big data in psychiatry: toward clinical applications
Rutledge, R. B., Chekroud, A. M., & Huys, Q. J. M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.006 · Cited by 195 · see citing works
Self-citationLin, E., Kuo, P., Liu, Y.-L., Yu, Y. W. ‐., Yang, A. C., & Tsai, S. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00290 · Cited by 191 · see citing works
Quantifying performance of machine learning methods for neuroimaging data
Jollans, L., Boyle, R. T., Eric Artiges, A., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 165 · see citing works
Lin, E., Lin, C., & Lane, H. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030969 · Cited by 170 · see citing works
Sajjadian, M., Lam, R. W., Milev, R., Rotzinger, S., Frey, B., Soares, C. N., Parikh, S., Foster, J., Turecki, G., Müller, D. J., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721003871 · Cited by 126 · see citing works
Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Acharya, U. R., & Li, Y. (2023) · Brain Informatics · doi:10.1186/s40708-023-00188-6 · Cited by 156 · see citing works
Bartova, L., Dold, M., Kautzky, A., Fabbri, C., Spies, M., Serretti, A., Souery, D., Mendlewicz, J., Zohar, J., Montgomery, S., et al. (2019) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2019.1635270 · Cited by 152 · see citing works
Artificial intelligence in psychiatry research, diagnosis, and therapy
Sun, J., Dong, Q., Wang, S.-W., Zheng, Y., Liu, X., Lu, T., Yuan, K., Shi, J., Hu, B., Hu, B., et al. (2023) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2023.103705 · Cited by 151 · see citing works
Kautzky, A., Dold, M., Bartova, L., Spies, M., Kranz, G. S., Souery, D., Montgomery, S., Mendlewicz, J., Zohar, J., Fabbri, C., et al. (2019) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12959 · Cited by 148 · see citing works
Patterns in the human brain mosaic discriminate males from females
Chekroud, A. M., Ward, E. J., Rosenberg, M. D., & Holmes, A. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1523888113 · Cited by 148 · see citing works
Self-citationThe foundation and architecture of precision medicine in neurology and psychiatry
Hampel, H. J., Gao, P., Cummings, J. L., Toschi, N., Thompson, P. M., Hu, Y., Cho, M., & Vergallo, A. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2022.12.004 · Cited by 149 · see citing works
Hu, M.-Y., Shu, X.-H., Yu, G., Wu, X.-Y., Välimäki, M., & Feng, H. (2021) · Journal of Medical Internet Research · doi:10.2196/20298 · Cited by 132 · see citing works
Recommendations and future directions for supervised machine learning in psychiatry
Cearns, M., Hahn, T., & Baune, B. T. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0607-2 · Cited by 127 · see citing works
Amare, A. T., Schubert, K. O., & Baune, B. T. (2017) · The EPMA Journal · doi:10.1007/s13167-017-0112-8 · Cited by 129 · see citing works
Data Mining Algorithms and Techniques in Mental Health: A Systematic Review
Alonso, S. G., Díez, I. D. L. T., Hamrioui, S., López-Coronado, M., Barreno, D. C., Nozaleda, L. M., & Franco, M. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1018-2 · Cited by 127 · see citing works
Deep-learning model for predicting 30-day postoperative mortality
Fritz, B. A., Cui, Z., Zhang, M., He, Y., Chen, Y., Kronzer, A., Ben Abdallah, A., King, C. R., & Avidan, M. S. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.07.025 · Cited by 127 · see citing works
Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A. T. F., Schoevers, R. A., van Hemert, A. M., Penninx, B. W. J. H., & Schmaal, L. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0289-1 · Cited by 125 · see citing works
Ding, X., Yue, X., Zheng, R., Bi, C., Li, D., & Yao, G. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.03.058 · Cited by 123 · see citing works
Cohen, Z. D., Kim, T. T., Van, H. L., Dekker, J., & Driessen, E. (2019) · Psychotherapy Research · doi:10.1080/10503307.2018.1563312 · Cited by 122 · see citing works
Network Analysis: A Novel Approach to Understand Suicidal Behaviour
Beurs, D. P. D. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14030219 · Cited by 117 · see citing works
Athreya, A. P., Neavin, D. R., Carrillo‐Roa, T., Skime, M. K., Biernacka, J. M., Frye, M. A., Rush, A. J., Wang, L., Binder, E. B., Iyer, R. K., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1482 · Cited by 114 · see citing works
Prediction, Not Association, Paves the Road to Precision Medicine
Bzdok, D., Varoquaux, G. P., & Steyerberg, E. W. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.2549 · Cited by 117 · see citing works
Buckman, J. E. J., Saunders, R., Stott, J. C. H., Arundell, L.-L., O’Driscoll, C., Davies, M. R., Eley, T. C., Hollon, S. D., Kendrick, T., Ambler, G., et al. (2021) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796021000342 · Cited by 114 · see citing works
Taking Psychiatry Research Online
Gillan, C. M., & Daw, N. D. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.002 · Cited by 110 · see citing works
What big data can do for treatment in psychiatry
Gillan, C. M., & Whelan, R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.003 · Cited by 106 · see citing works
Tornero-Costa, R., Martínez-Millana, A., Muscat, N. A., Lazëri, L., Traver, V., & Novillo-Ortiz, D. (2023) · JMIR Mental Health · doi:10.2196/42045 · Cited by 118 · see citing works
Kim, Y., & Na, K. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.06.024 · Cited by 102 · see citing works
Predicting relapse after antidepressant withdrawal – a systematic review
Berwian, I. M., Walter, H., Seifritz, E., & Huys, Q. J. M. (2017) · Psychological Medicine · doi:10.1017/s0033291716002580 · Cited by 93 · see citing works
Mohamed, E. S., Naqishbandi, T. A., Bukhari, S. A. C., Rauf, I., Sawrikar, V., & Hussain, A. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100185 · Cited by 115 · see citing works
Fusar-Poli, P., Werbeloff, N., Rutigliano, G., Oliver, D., Davies, C., Stahl, D., McGuire, P., & Osborn, D. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sby070 · Cited by 95 · see citing works
The revolution of personalized psychiatry: will technology make it happen sooner?
Perna, G., Grassi, M., Caldirola, D., & Nemeroff, C. B. (2017) · Psychological Medicine · doi:10.1017/s0033291717002859 · Cited by 99 · see citing works
Single classifier vs. ensemble machine learning approaches for mental health prediction
Chung, J., & Teo, J. (2023) · Brain Informatics · doi:10.1186/s40708-022-00180-6 · Cited by 99 · see citing works
Kambeitz‐Ilankovic, L., Betz, L. T., Dominke, C., Haas, S. S., Subramaniam, K., Fisher, M., Vinogradov, S., Koutsouleris, N., & Kambeitz, J. P. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.031 · Cited by 99 · see citing works
Socioeconomic Indicators of Treatment Prognosis for Adults With Depression
Buckman, J. E. J., Saunders, R., Stott, J. C. H., Cohen, Z. D., Arundell, L.-L., Eley, T. C., Hollon, S. D., Kendrick, T., Ambler, G., Watkins, E. R., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0100 · Cited by 97 · see citing works
Welch, V. E., Wy, T. J., Ligezka, A. N., Hassett, L. C., Croarkin, P. E., Athreya, A. P., & Romanowicz, M. (2022) · Journal of Medical Internet Research · doi:10.2196/33560 · Cited by 97 · see citing works
Using machine learning-based analysis for behavioral differentiation between anxiety and depression
Richter, T., Fishbain, B., Markus, A., Richter‐Levin, G., & Okon‐Singer, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-72289-9 · Cited by 96 · see citing works
Taliaz, D., Spinrad, A., Barzilay, R., Barnett-Itzhaki, Z., Averbuch, D., Teltsh, O., Schurr, R., Darki-Morag, S., & Lerer, B. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01488-3 · Cited by 93 · see citing works
Gillan, C. M., Kalanthroff, E., Evans, M., Weingarden, H. M., Jacoby, R. J., Gershkovich, M., Snorrason, Í., Campeas, R. B., Cervoni, C., Crimarco, N., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.2998 · Cited by 89 · see citing works
Zhdanov, A. V., Atluri, S., Wong, W., Vaghei, Y., Daskalakis, Z. J., Blumberger, D. M., Frey, B. N., Giacobbe, P., Lam, R. W. M., Milev, R. V., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.18377 · Cited by 93 · see citing works
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91 · see citing works
GWAS-based machine learning approach to predict duloxetine response in major depressive disorder
Maciukiewicz, M., Marshe, V. S., Hauschild, A.-C., Foster, J. A., Rotzinger, S., Kennedy, J. L., Kennedy, S. H., Müller, D. J., & Geraci, J. (2018) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.12.009 · Cited by 91 · see citing works
A systematic review on automated clinical depression diagnosis
Mao, K., Wu, Y., & Chen, J. (2023) · npj Mental Health Research · doi:10.1038/s44184-023-00040-z · Cited by 91 · see citing works
Browning, M., Kingslake, J., Dourish, C. T., Goodwin, G. M., Harmer, C. J., & Dawson, G. R. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.11.1102 · Cited by 83 · see citing works
Tran, B. X., McIntyre, R. S., Latkin, C. A., Phan, H. T., Vu, G. T., Nguyen, H. L. T., Gwee, K. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16122150 · Cited by 90 · see citing works
Smartphones and the Neuroscience of Mental Health
Gillan, C. M., & Rutledge, R. B. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-101220-014053 · Cited by 84 · see citing works
Supervised machine learning methods in psychology: A practical introduction with annotated R code
Rosenbusch, H., Soldner, F., Evans, A. M., & Zeelenberg, M. (2021) · Social and Personality Psychology Compass · doi:10.1111/spc3.12579 · Cited by 87 · see citing works
Pearson, R., Pisner, D., Meyer, B., Shumake, J., & Beevers, C. G. (2019) · Psychological Medicine · doi:10.1017/s003329171800315x · Cited by 81 · see citing works
Deep learning for the prediction of treatment response in depression
Squarcina, L., Villa, F. M., Nobile, M., Grisan, E., & Brambilla, P. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.11.104 · Cited by 84 · see citing works
Predicting childhood obesity using electronic health records and publicly available data
Hammond, R., Athanasiadou, R., Curado, S., Aphinyanaphongs, Y., Abrams, C. B., Messito, M. J., Gross, R. S., Katzow, M. W., Jay, M. R., Razavian, N., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215571 · Cited by 83 · see citing works
Machine Learning Methods for Predicting Postpartum Depression: Scoping Review
Saqib, K., Khan, A. F., & Butt, Z. A. (2021) · JMIR Mental Health · doi:10.2196/29838 · Cited by 89 · see citing works
Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges
Ienca, M., & Ignatiadis, K. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740352 · Cited by 84 · see citing works
Liu, Y., Hu, P., Zheng, Z.-H., Zhong, D., Xie, W., Tang, Z., Pan, B., Luo, J. F., Zhang, W., & Wang, X. (2021) · Advanced Materials · doi:10.1002/adma.202108525 · Cited by 81 · see citing works
The Psychedelic Future of Post-Traumatic Stress Disorder Treatment
Zaretsky, T. G., Jagodnik, K. M., Barsic, R., Antonio, J. H., Bonanno, P. A., MacLeod, C., Pierce, C., Carney, H., Morrison, M. T., Saylor, C. F., et al. (2024) · Current Neuropharmacology · doi:10.2174/1570159x22666231027111147 · Cited by 85 · see citing works
Sakurai, H., Suzuki, T., Yoshimura, K., Mimura, M., & Uchida, H. (2017) · Psychopharmacology · doi:10.1007/s00213-017-4634-5 · Cited by 80 · see citing works
Comprehensive review: Computational modelling of schizophrenia
Valton, V., Romaniuk, L., Steele, J. D., Lawrie, S. M., & Seriès, P. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.08.022 · Cited by 76 · see citing works
Nashwan, A. J., Gharib, S., Alhadidi, M. M. B., El‐Ashry, A. M., Alamgir, A., Al-Hassan, M., Khedr, M. A., Dawood, S. S., & Abufarsakh, B. (2023) · Issues in Mental Health Nursing · doi:10.1080/01612840.2023.2263579 · Cited by 82 · see citing works
Nicolini, M. E., Kim, S. Y. H., Churchill, M. E., & Gastmans, C. (2020) · Psychological Medicine · doi:10.1017/s0033291720001543 · Cited by 77 · see citing works
Bachstetter, A. D., Bodnar, C., & Morganti, J. M. (2018) · Neural Regeneration Research · doi:10.4103/1673-5374.238604 · Cited by 78 · see citing works
Oh, J., Yun, K., Maoz, U., Kim, T.-S., & Chae, J. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.06.034 · Cited by 78 · see citing works
Ebert, D. D., Harrer, M., Apolinário-Hagen, J., & Baumeister, H. (2019) · Advances in Experimental Medicine and Biology · doi:10.1007/978-981-32-9721-0_29 · Cited by 76 · see citing works
Hornstein, S., Forman‐Hoffman, V. L., Nazander, A., Ranta, K., & Hilbert, K. (2021) · Digital Health · doi:10.1177/20552076211060659 · Cited by 79 · see citing works
Paul, R., Andlauer, T. F. M., Czamara, D., Hoehn, D., Lucae, S., Pütz, B., Lewis, C. M., Uher, R., Müller‐Myhsok, B., Ising, M., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0524-4 · Cited by 72 · see citing works
A bibliometric analysis and visualization of medical data mining research
Hu, Y., Yu, Z., Cheng, X., Luo, Y. G., & Wen, C. (2020) · Medicine · doi:10.1097/md.0000000000020338 · Cited by 72 · see citing works
Buckman, J. E. J., Saunders, R., Cohen, Z. D., Barnett, P., Clarke, K., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721001367 · Cited by 70 · see citing works
Gauthier, G., Guérin, A., Zhdanava, M., Jacobson, W., Nomikos, G., Merikle, E., François, C., & Perez, V. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1385-0 · Cited by 70 · see citing works
White Matter Abnormalities in Never-Treated Patients With Long-Term Schizophrenia
Xiao, Y., Sun, H., Shi, S., Jiang, D., Bo, T., Zhao, Y., Zhang, W., Gong, Q., Sweeney, J. A., & Lui, S. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17121402 · Cited by 70 · see citing works
Gueorguieva, R., Chekroud, A. M., & Krystal, J. H. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30038-x · Cited by 67 · see citing works
Self-citationTransforming Psychiatry into Data-Driven Medicine with Digital Measurement Tools
Hsin, H., Fromer, M., Peterson, B. E., Walter, C., Fleck, M. S., Campbell, A. T., Varghese, P., & Califf, R. M. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0046-0 · Cited by 65 · see citing works
Prediction of response to drug therapy in psychiatric disorders
Stern, S., Linker, S. B., Vadodaria, K. C., Marchetto, M. C., & Gage, F. H. (2018) · Open Biology · doi:10.1098/rsob.180031 · Cited by 65 · see citing works
- Authors
- 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.
- Affiliations
- Yale New Haven Hospital (US)
- Capital One (United States) (US)
- Yale University (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Chekroud, Adam Mourad
- ORCID
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 3
- Issue
- 3
- Pages
- 243-250
- ISSN
- 2215-0366 (ISSN-L)
- OpenAlex
- W2257438637
- Cited by
- 676
- References
- 49
- Citations by year
- 2026: 57 · 2025: 86 · 2024: 53 · 2023: 70 · 2022: 74 · 2021: 79 · 2020: 87 · 2019: 76 · 2018: 42 · 2017: 34 · 2016: 17
- FWCI
- 45.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- depression, antidepressant treatment response prediction, citalopram, escitalopram, clinical remission, STAR*D, venlafaxine, mirtazapine, patient-reported outcomes, treatment matching, machine learning prediction models, external validation
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Algorithms, Antidepressive Agents, Major Depressive Disorder, Humans, Middle Aged, Models, Statistical, Citalopram, Treatment Outcome, Cross-Over Studies, Young Adult
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