Cited by 676Treatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.

Cross-trial prediction of treatment outcome in depression: a machine learning approach

Chekroud, Adam Mourad, Zotti, Ryan Joseph, Shehzad, Zarrar, Gueorguieva, Ralitza, Johnson, Marcia K, Trivedi, Madhukar H, Cannon, Tyrone D, Krystal, John Harrison, Corlett, Philip Robert·2016·The Lancet Psychiatry

DOI 10.1016/s2215-0366(15)00471-x

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1016/s2215-0366(15)00471-x

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
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
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
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
Applied Regression Analysis
Draper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993
Building Predictive Models in R Using the caret Package
Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863
Stochastic gradient boosting
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Applied Predictive Modeling
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788
Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice
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
Self-citation
APACHE—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

Show all 49 references

Sorted by citations

Items 201–300 of 674

Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study
Liang, S., Deng, W., Li, X., Wang, Q., Greenshaw, A. J., Guo, W., Kong, X., Li, M., Zhao, L., Meng, Y., et al. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.03.022 · Cited by 29
Differential Treatment Benet Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data
Mehltretter, J., Fratila, R., Benrimoh, D. A., Kapelner, A. D., Perlman, K., Snook, E., Israel, S. A., Armstrong, C., Miresco, M., & Turecki, G. (2020) · Computational Psychiatry · doi:10.1162/cpsy_a_00029 · Cited by 29
Machine learning to advance the prediction, prevention and treatment of eating disorders
Wang, S. B. (2021) · European Eating Disorders Review · doi:10.1002/erv.2850 · Cited by 35
Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample
Dipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S., Jacka, F. N., & Meyer, D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167055 · Cited by 29
Clinical patterns differentially predict response to transcranial direct current stimulation (tDCS) and escitalopram in major depression: A machine learning analysis of the ELECT-TDCS study
Kambeitz, J. P., Goerigk, S. A., Gattaz, W. F., Falkai, P., Benseñor, I. M., Lotufo, P. A., Bühner, M., Koutsouleris, N., Padberg, F., & Brunoni, A. R. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.118 · Cited by 31
Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review
Prégent, J., Chung, V.-H.-A., Adib, I. E., Désilets, M., & Hudon, A. (2025) · JMIR Medical Education · doi:10.2196/75238 · Cited by 40
Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM)
Dipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S., Jacka, F. N., & Meyer, D. (2016) · European Psychiatry · doi:10.1016/j.eurpsy.2016.06.003 · Cited by 28
A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study
Lin, S., Wu, Y., & Fang, Y. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04439-4 · Cited by 32
Systematic review of machine learning techniques to predict anxiety and stress in college students
Vergaray, A. D., Saboya, N., Necochea-Chamorro, J. I., Ramos, K. J. Z., & Valencia, Y. D. R. V. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101391 · Cited by 27
Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology
Mulat, M., Banicod, R. J. S., Tabassum, N., Javaid, A., Kim, T., Kim, Y., & Khan, F. (2025) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2025.107232 · Cited by 31
Beyond symptom improvement: transdiagnostic and disorder‐specific ways to assess functional and quality of life outcomes across mental disorders in adults
Correll, C. U., Cortese, S., Solmi, M., Boldrini, T., Demyttenaere, K., Domschke, K., Fusar‐Poli, P., Gorwood, P. A. P. M., Harvey, P. D., Keefe, R. S. E., et al. (2025) · World Psychiatry · doi:10.1002/wps.21338 · Cited by 33
Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models
Symons, M., Feeney, G. F. X., Gallagher, M. R., Young, R. M., & Connor, J. P. (2020) · Addiction · doi:10.1111/add.15038 · Cited by 30
Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients
Oliver, D., Wong, C. M. J., Bøg, M., Jönsson, L., Kinon, B. J., Wehnert, A., Jørgensen, K. T., Irving, J., Stahl, D. R., McGuire, P. K., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01032-9 · Cited by 29
A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating
Krug, I., Linardon, J., Greenwood, C. J., Youssef, G., Treasure, J. L., Fernández‐Aranda, F., Karwautz, A. F. K., Wagner, G., Collier, D. A., Anderluh, M., et al. (2021) · Psychological Medicine · doi:10.1017/s003329172100489x · Cited by 27
Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study
Lin, S., Wu, Y., & Fang, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.764806 · Cited by 28
The perilous path from publication to practice
Chekroud, A. M., & Koutsouleris, N. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.227 · Cited by 26
Self-citation
Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
Hughes, M. C., Pradier, M. F., Ross, A. S., McCoy, T. H., Perlis, R. H., & Doshi‐Velez, F. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.5308 · Cited by 25
Violent and non-violent offending in patients with schizophrenia: Exploring influences and differences via machine learning
Sonnweber, M., Lau, S., & Kirchebner, J. (2021) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2021.152238 · Cited by 27
A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder
Tanguay-Sela, M., Rollins, C. P. E., Pérez, T., Qiang, V., Golden, G. M., Tunteng, J.-F., Perlman, K., Simard, J., Benrimoh, D. A., & Margolese, H. C. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.08.041 · Cited by 32
The Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication
Cohen, Z. D., DeRubeis, R. J., Hayes, R., Watkins, E. R., Lewis, G., Byng, R., Byford, S., Crane, C., Kuyken, W., Dalgleish, T., et al. (2023) · Clinical Psychological Science · doi:10.1177/21677026221076832 · Cited by 25
The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis
Chen, Y., Zhao, W., Yi, S., & Liu, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1174080 · Cited by 25
Predictive modeling for response to lithium and quetiapine in bipolar disorder
Kim, T. T., Dufour, S. C., Xu, C., Cohen, Z. D., Sylvia, L. G., Deckersbach, T., DeRubeis, R. J., & Nierenberg, A. A. (2019) · Bipolar Disorders · doi:10.1111/bdi.12752 · Cited by 24
Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
Beacher, F. D., Mujica-Parodi, L. R., Gupta, S., & Ancora, L. A. (2021) · Algorithms · doi:10.3390/a14050147 · Cited by 25
Development and validation of a machine learning-based vocal predictive model for major depressive disorder
Wasserzug, Y., Degani, Y., Bar-Shaked, M., Binyamin, M., Klein, A., Hershko, S., & Levkovitch, Y. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.12.117 · Cited by 25
The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation
Lee, C. T., Palacios, J. E., Richards, D., Hanlon, A. K., Lynch, K., Harty, S., Claus, N., Swords, L., O’Keane, V., Stephan, K. E., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-022-04462-5 · Cited by 24
Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning
Ricka, N., Pellegrin, G., Fompeyrine, D. A., Lahutte, B., & Geoffroy, P. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-33359-w · Cited by 24
Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?
Shani, R., Tal, S., Zilcha‐Mano, S., & Okon‐Singer, H. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00064 · Cited by 29
Using the Personalized Advantage Index for individual treatment allocation to cognitive behavioral therapy (CBT) or a CBT with integrated exposure and emotion-focused elements (CBT-EE)
Friedl, N., Berger, T., Krieger, T., Caspar, F., & Holtforth, M. G. (2019) · Psychotherapy Research · doi:10.1080/10503307.2019.1664782 · Cited by 18
Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning
Meinlschmidt, G., Tegethoff, M., Belardi, A., Stalujanis, E., Oh, M., Jung, E., Kim, H., Yoo, S., & Lee, J. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.071 · Cited by 25
Automated Assessment of Psychiatric Disorders Using Speech: A Systematic ReviewPreprint
Low, D. M., Bentley, K. H., & Ghosh, S. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5pwze · Cited by 30
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
Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial
Gunn, J. M., Wachtler, C., Fletcher, S. L., Davidson, S. K., Mihalopoulos, C., Palmer, V. J., Hegarty, K. L., Coe, A., Murray, E., Dowrick, C. F., et al. (2017) · Trials · doi:10.1186/s13063-017-2089-y · Cited by 24
A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy
Roger, É., Torlay, L., Gardette, J., Mosca, C., Banjac, S., Minotti, L., Kahane, P., & Baciu, M. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107455 · Cited by 24
AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness
Wiese, W., & Friston, K. J. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113704 · Cited by 27
Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder
Ho, C. S. H., Wang, J., Tay, G. W. N., Ho, R., Lin, H., Li, Z., & Chen, N. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03224-7 · Cited by 23
Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis
Curtiss, J. E., & DiPietro, C. P. (2025) · Clinical Psychology Review · doi:10.1016/j.cpr.2025.102593 · Cited by 23
Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials
Passos, I. C., & Mwangi, B. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0250-y · Cited by 24
Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1
Nuñez, J.-J., Nguyen, T. T., Zhou, Y., Cao, B., Ng, R. T., Chen, J., Frey, B. N., Milev, R. V., Müller, D. J., Rotzinger, S., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253023 · Cited by 26
Barriers and solutions to the adoption of translational tools for computational psychiatry
Benrimoh, D. A., Fisher, V., Mourgues, C., Sheldon, A. D., Smith, R. S., & Powers, A. R. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02114-y · Cited by 27
Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care
Maslej, M. M., Kloiber, S. M., Ghassemi, M., Yu, J., & Hill, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02509-z · Cited by 29
Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study
Chondros, P., Davidson, S. K., Wolfe, R. S., Gilchrist, G., Dowrick, C. F., Griffiths, F. E., Hegarty, K. L., Herrman, H., & Gunn, J. M. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.11.042 · Cited by 23
Machine learning probability calibration for high-risk clinical decision-making
Cearns, M., Hahn, T., Clark, S. R., & Baune, B. T. (2019) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867419885448 · Cited by 23
Machine Learning for Clinical Psychology and Clinical Neuroscience
Coutanche, M. N., & Hallion, L. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781316995808.041 · Cited by 24
Mitochondrial function parameters as a tool for tailored drug treatment of an individual with psychosis: a proof of concept study
Bar-Yosef, T., Hussein, W., Yitzhaki, O., Damri, O., Givon, L., Marom, C., Gurman, V., Levine, J., Bersudsky, Y., Agam, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69207-4 · Cited by 22
Prognostic Accuracy of DSM-5 Attenuated Psychosis Syndrome in Adolescents: Prospective Real-World 5-Year Cohort Study
Mensi, M. M., Molteni, S., Iorio, M., Filosi, E., Ballante, E., Balottin, U., Fusar‐Poli, P., & Borgatti, R. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab041 · Cited by 22
Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning
Soldatos, R. F., Cearns, M., Nielsen, M. Ø., Kollias, C. T., Xenaki, L., Stefanatou, P., Ralli, I., Dimitrakopoulos, S., Hatzimanolis, A., Kosteletos, I., et al. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab107 · Cited by 22
Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder
Zou, B., Zhang, X., Xiao, L., Bai, R., Li, X., Liang, H., Ma, H., & Wang, G. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3260301 · Cited by 26
Machine learning models for predicting depression in Korean young employees
Kim, S., Gil, M., & Min, E. J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1201054 · Cited by 24
Predicting Personalized Risk of Mood Recurrences in Youths and Young Adults With Bipolar Spectrum Disorder
Birmaher, B. B., Merranko, J. A., Gill, M. K., Hafeman, D., Goldstein, T. R., Goldstein, B. I., Hower, H. M., Strober, M. A., Axelson, D., Ryan, N. D., et al. (2020) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.12.005 · Cited by 22
Personalized Characterization of Emotional States in Patients with Bipolar Disorder
Llamocca, P., López, V., Santos, M., & Čukić, M. (2021) · Mathematics · doi:10.3390/math9111174 · Cited by 23
Primary care physicians’ perceptions of artificial intelligence systems in the care of adolescents’ mental health
Ghadiri, P., Yaffe⃰, M. J., Adams, A. M., & Rahimi, S. A. (2024) · BMC Family Practice · doi:10.1186/s12875-024-02417-1 · Cited by 24
Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission
Davey, C. G., Cearns, M., Jamieson, A. J., & Harrison, B. J. (2021) · Psychological Medicine · doi:10.1017/s0033291721004323 · Cited by 21
On the role of artificial intelligence in psychiatry
Rocheteau, E. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2022.132 · Cited by 23
Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder
Ju, Y., Wang, M., Liu, J., Liu, B., Yan, D., Lu, X., Sun, J., Dong, Q., Zhang, L., Guo, H., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722002628 · Cited by 22
Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care
Scodari, B. T., Chacko, S. M., Matsumura, R., & Jacobson, N. C. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.004 · Cited by 21
Artificial intelligence and psychotherapy: A counterpoint
Richards, D. (2024) · Counselling and Psychotherapy Research · doi:10.1002/capr.12758 · Cited by 29
Using machine learning to predict outcomes in psychosis
Young, J., Kempton, M. J., & McGuire, P. K. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30218-8 · Cited by 21
Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders
Levchenko, А., Nurgaliev, T., Kanapin, A. A., Samsonova, A. A., & Gainetdinov, R. R. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03990 · Cited by 20
External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study
Nigatu, Y. T., Liu, Y., & Wang, J. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-0971-x · Cited by 18
The neuroscience of mental illness: Building toward the future
Gordon, J. A., Dzirasa, K., & Petzschner, F. H. (2024) · Cell · doi:10.1016/j.cell.2024.09.028 · Cited by 23
Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review
Wójcik, Z., Dimitrova, V. G., Warrington, L., Velikova, G., & Absolom, K. L. (2025) · Health and Quality of Life Outcomes · doi:10.1186/s12955-025-02365-z · Cited by 25
Integration of Machine Learning and Pharmacogenomic Biomarkers for Predicting Response to Antidepressant Treatment: Can Computational Intelligence be used to Augment Clinical Assessments?
Athreya, A. P., Iyer, R. K., Wang, L., Weinshilboum, R. M., & Bobo, W. V. (2019) · Pharmacogenomics · doi:10.2217/pgs-2019-0119 · Cited by 21
Cross-trial prediction of depression remission using problem-solving therapy: A machine learning approach
Kannampallil, T., Dai, R., Lv, N., Xiao, L., Lu, C., Ajilore, O., Snowden, M. B., Venditti, E. M., Williams, L. M., Kringle, E. A., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.04.015 · Cited by 20
Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance
the ACT NOW Study Investigators, Hu, X., Reaven, P. D., Saremi, A., Liu, N., Abbasi, M. A., Liu, H., & Migrino, R. Q. (2016) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/s13637-016-0049-6 · Cited by 20
AI for mental health: clinician expectations and priorities in computational psychiatry
Fischer, L., Mann, P. A., Nguyen, M.-H. H., Becker, S., Khodadadi, S. H. A., Schulz, A., Thanarajah, S. E., Repple, J., Hahn, T., Reif, A., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06957-3 · Cited by 23
Computational Psychiatry: Embracing Uncertainty and Focusing on Individuals, Not Averages
Chekroud, A. M., Lane, C. E., & Ross, D. A. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.07.011 · Cited by 19
Self-citation
Medication appropriateness criteria for older adults: a narrative review of criteria and supporting studies
Niehoff, K. M., Mecca, M. C., & Fried, T. R. (2019) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098618815431 · Cited by 21
From multivariate methods to an AI ecosystem
Winter, N. R., Cearns, M., Clark, S. R., Leenings, R., Dannlowski, U., Baune, B. T., & Hahn, T. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01116-y · Cited by 20
Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report
Harris, J. K., Hassel, S., Davis, A. D., Zamyadi, M., Arnott, S. R., Milev, R. V., Lam, R. W. M., Frey, B. N., Hall, G. B., Müller, D. J., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103120 · Cited by 19
Providing the most appropriate care to our individual patients
Cipriani, A., & Tomlinson, A. (2019) · Evidence-Based Mental Health · doi:10.1136/ebmental-2019-300080 · Cited by 19
Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning
Smith, J. B., Shew, M. A., Karadaghy, O. A., Nallani, R., Sykes, K. J., Gan, G. N., Brant, J. A., & Bur, A. M. (2020) · Head & Neck · doi:10.1002/hed.26246 · Cited by 17
Predictors and moderators of treatment dropout in cognitive-behavioral and psychodynamic therapies for panic disorder
Keefe, J. R., Chambless, D., Barber, J., & Milrod, B. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1784487 · Cited by 9
Reengineering Clinical Decision Support Systems for Artificial Intelligence
Strachna, O., & Asan, O. (2020) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi48887.2020.9374370 · Cited by 18
Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis
Worthington, M., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Keshavan, M. S., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Stone, W. S., et al. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab115 · Cited by 18
Self-citation
Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach
Curtiss, J. E., Bernstein, E. E., Wilhelm, S., & Phillips, K. A. (2022) · Psychological Medicine · doi:10.1017/s0033291721005390 · Cited by 18
Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach
Vetter, J., Schultebraucks, K., Galatzer‐Levy, I. R., Boeker, H., Brühl, A. B., Seifritz, E., & Kleim, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-09226-5 · Cited by 21
Machine Learning–Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation
Crabb, B. T., Hamrick, F. A., Campbell, J. M., Vignolles‐Jeong, J., Magill, S. T., Prevedello, D. M., Carrau, R. L., Otto, B. A., Hardesty, D. A., Couldwell, W. T., et al. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001967 · Cited by 18
Brain activity measured by functional brain imaging predicts breathlessness improvement during pulmonary rehabilitation
Finnegan, S., Browning, M. D., Duff, E. P., Harmer, C. J., Reinecke, A., Rahman, N. M., & Pattinson, K. T. S. (2022) · Thorax · doi:10.1136/thorax-2022-218754 · Cited by 18
Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records
Xu, Z., Vekaria, V., Wang, F., Cukor, J., Su, C., Adekkanattu, P. M., Brandt, P., Jiang, G., Kiefer, R. C., Luo, Y., et al. (2023) · Psychiatric Research and Clinical Practice · doi:10.1176/appi.prcp.20220015 · Cited by 18
Anxiety, Depression and Stress prediction among College Students using Machine Learning Algorithms
Malik, S. S., & Khan, A. (2023) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict56924.2023.10157693 · Cited by 18
Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies
Benrimoh, D. A., Kleinerman, A., Furukawa, T. A., Reynolds, C. F., Lenze, E. J., Karp, J. F., Mulsant, B. H., Armstrong, C., Mehltretter, J., Fratila, R., et al. (2023) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2023.09.009 · Cited by 18
Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining
Shameer, K., Pérez-Rodríguez, M. M., Bachar, R., Li, L., Johnson, A. M., Johnson, K. W., Glicksberg, B. S., Smith, M. R., Readhead, B., Scarpa, J. R., et al. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0653-3 · Cited by 20
Machine Learning in Neural Networks
Lin, E., & Tsai, S. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9721-0_7 · Cited by 20
Assessing the potential of national strategies for electronic health records for population health monitoring and research.
Friedman, D. J. (2006) · PubMed · Cited by 18
Machine learning to predict clinical remission in depressed patients after acute phase selective serotonin reuptake inhibitor treatment
Zhou, S., Ma, Q., Lou, Y., Lv, X., Tian, H., Wei, J., Zhang, K., Zhu, G., Chen, Q., Si, T., et al. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.03.079 · Cited by 18
Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome
Kim, I. B., & Park, S. (2021) · Diagnostics · doi:10.3390/diagnostics11091631 · Cited by 16
Global trends of machine learning applications in psychiatric research over 30 years: A bibliometric analysis
Baminiwatta, A. (2021) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2021.102986 · Cited by 17
Using machine learning to predict heavy drinking during outpatient alcohol treatment
Roberts, W., Zhao, Y., Verplaetse, T. L., Moore, K. E., Peltier, M. R., Burke, C., Zakiniaeiz, Y., & McKee, S. A. (2022) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14802 · Cited by 21
Advancing mental health care with AI-enabled precision psychiatry tools: A patent review
Kleine, A., Lermer, E., Cecil, J., Heinrich, A., & Gaube, S. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100322 · Cited by 19
Data mining algorithm predicts a range of adverse outcomes in major depression
van Loo, H. M., Bigdeli, T. B., Milaneschi, Y., Aggen, S. H., & Kendler, K. S. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.07.098 · Cited by 17
Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression
Ali, F., Wengler, K., He, X., Nguyen, M. H., Parsey, R. V., & DeLorenzo, C. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100110 · Cited by 17
From neuroscience to evidence based psychological treatments – The promise and the challenge, ECNP March 2016, Nice, France
Goodwin, G. M., Holmes, E. A., Andersson, E., Browning, M. D., Jones, A., Lass‐Hennemann, J., Månsson, K. N. T., Moessnang, C., Salemink, E., Sánchez-López, Á., et al. (2018) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2017.10.036 · Cited by 18
Development of the treatment prediction model in the artificial intelligence in depression – medication enhancement study
Benrimoh, D. A., Armstrong, C., Mehltretter, J., Fratila, R., Perlman, K., Israel, S. A., Kapelner, A. D., Parikh, S. V., Karp, J. F., Heller, K., et al. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00136-8 · Cited by 16
Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance
Colombo, F., Calesella, F., Bravi, B., Fortaner-Uyà, L., Monopoli, C., Tassi, E., Carminati, M., Zanardi, R., Bollettini, I., Poletti, S., et al. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.05.015 · Cited by 18
Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning
Yoo, A., Li, F., Youn, J., Guan, J., Guyer, A. E., Hostinar, C. E., & Tagkopoulos, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-72158-9 · Cited by 18
Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain
Norbury, A., & Seymour, B. (2018) · F1000Research · doi:10.12688/f1000research.13723.2 · Cited by 16
Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study
Damiani, S., Rutigliano, G., Fazia, T., Merlino, S., Berzuini, C., Bernardinelli, L., Politi, P., & Fusar‐Poli, P. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab070 · Cited by 18
Can machine learning identify childhood characteristics that predict future development of bipolar disorder a decade later?
Uchida, M., Bukhari, Q., DiSalvo, M., Green, A., Serra, G., Vater, C. H., Ghosh, S., Faraone, S. V., Gabrieli, J. D. E., & Biederman, J. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.09.051 · Cited by 16
Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation
Li, C., Chen, C.-S., Cheng, C., Chen, C.-P., Chen, J.-P., Chen, M., Bai, Y., & Tsai, S. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.059 · Cited by 16
Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets
Perlman, K., Mehltretter, J., Benrimoh, D. A., Armstrong, C., Fratila, R., Popescu, C., Tunteng, J.-F., Williams, J. D., Rollins, C. P. E., Golden, G. M., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02970-4 · Cited by 15

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: Chekroud, Adam Mourad
Shehzad, Zarrar
Gueorguieva, Ralitza
Trivedi, Madhukar H
Cannon, Tyrone D
Krystal, John Harrison
Corlett, Philip Robert
Venue: The Lancet Psychiatry
Publisher: Elsevier BV
Published: 2016-03
Volume: 3
Issue: 3
Pages: 243-250
ISSN: 2215-0366 (ISSN-L)
DOI: 10.1016/s2215-0366(15)00471-x
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: The Lancet Psychiatry (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Pharmacology (medical) › 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

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

Cross-trial prediction of treatment outcome in depression: a machine learning approach

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks