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Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

Published online by Cambridge University Press:  12 October 2021

Mehri Sajjadian ,
Raymond W. Lam ,
Roumen Milev ,
Susan Rotzinger ,
Benicio N. Frey ,
Claudio N. Soares ,
Sagar V. Parikh ,
Jane A. Foster ,
Gustavo Turecki  and
Daniel J. Müller
...Show all authors
Mehri Sajjadian
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
Raymond W. Lam
Affiliation:
Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
Roumen Milev
Affiliation:
Department of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
Susan Rotzinger
Affiliation:
Department of Psychiatry, University of Toronto, Toronto, ON, Canada Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
Benicio N. Frey
Affiliation:
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
Claudio N. Soares
Affiliation:
Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
Sagar V. Parikh
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Jane A. Foster
Affiliation:
Department of Psychiatry & Behavioural Neurosciences, St. Joseph's Healthcare, Hamilton, ON, Canada
Gustavo Turecki
Affiliation:
Department of Psychiatry, Douglas Institute, McGill University, Montreal, QC, Canada
Daniel J. Müller
Affiliation:
Campbell Family Mental Health Research Institute, Center for Addiction and Mental Health, Toronto, ON, Canada Department of Psychiatry, University of Toronto, Toronto, ON, Canada
Stephen C. Strother
Affiliation:
Baycrest and Department of Medical Biophysics, Rotman Research Center, University of Toronto, Toronto, ON, Canada
Faranak Farzan
Affiliation:
eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
Sidney H. Kennedy
Affiliation:
Department of Psychiatry, University of Toronto, Toronto, ON, Canada Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada Department of Psychiatry, University Health Network, Toronto, ON, Canada Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada
Rudolf Uher*
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
*
Author for correspondence: Rudolf Uher, E-mail: uher@dal.ca
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Abstract

Background

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Methods

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Results

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

Conclusions

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Keywords

Machine learningMDDmeta-analysispredictive analysissystematic reviewtreatment outcome
Type
Review Article
Information
Psychological Medicine , Volume 51 , Issue 16 , December 2021 , pp. 2742 - 2751
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Al-Harbi, K. S. (2012). Treatment-resistant depression: Therapeutic trends, challenges, and future directions. Patient Preference and Adherence, 6, 369–388. https://doi.org/10.2147/PPA.S29716.CrossRefGoogle Scholar
Athreya, A. P., Neavin, D., Carrillo-Roa, T., Skime, M., Biernacka, J., Frye, M. A., … Bobo, W. V. (2019). Pharmacogenomics-driven prediction of antidepressant treatment outcomes: A machine-learning approach with multi-trial replication. Clinical Pharmacology and Therapeutics, 106(4), 855–865. https://doi.org/10.1002/cpt.1482.CrossRefGoogle ScholarPubMed
Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454. https://doi.org/10.1038/533452A.CrossRefGoogle ScholarPubMed
Bartlett, E. A., DeLorenzo, C., Sharma, P., Yang, J., Zhang, M., Petkova, E., … Parsey, R. V. (2018). Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder. Neuropsychopharmacology, 43(11), 2221–2230. https://doi.org/10.1038/s41386-018-0122-9.CrossRefGoogle ScholarPubMed
Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., & Popp, J. (2013). Sample size planning for classification models. Analytica Chimica Acta, 760, 25–33. https://doi.org/10.1016/j.aca.2012.11.007.CrossRefGoogle ScholarPubMed
Bermingham, M. L., Pong-Wong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campbell, H., … Haley, C. S. (2015). Application of high-dimensional feature selection: Evaluation for genomic prediction in man. Scientific Reports, 5, 10312. https://doi.org/10.1038/srep10312.CrossRefGoogle ScholarPubMed
Bertsimas, D., Pawlowski, C., & Zhuo, Y. D. (2018). From predictive methods to missing data imputation: An optimization approach. Journal of Machine Learning Research, 18(196), 1–39.Google Scholar
Browning, M., Bilderbeck, A. C., Dias, R., Dourish, C. T., Kingslake, J., Deckert, J., … Dawson, G. R. (2021). The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): An open-label, randomised controlled trial. Neuropsychopharmacology, 46(7), 1307–1314. https://doi.org/10.1038/s41386-021-00981-z.CrossRefGoogle ScholarPubMed
Browning, M., Kingslake, J., Dourish, C. T., Goodwin, G. M., Harmer, C. J., & Dawson, G. R. (2019). Predicting treatment response to antidepressant medication using early changes in emotional processing. European Neuropsychopharmacology, 29(1), 66–75. https://doi.org/10.1016/j.euroneuro.2018.11.1102.CrossRefGoogle ScholarPubMed
Cawley, G. C., & Talbot, N. L. C. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11, 2079–2107.Google Scholar
Cepeda, M. S., Reps, J., Fife, D., Blacketer, C., Stang, P., & Ryan, P. (2018). Finding treatment-resistant depression in real-world data: How a data-driven approach compares with expert-based heuristics. Depression and Anxiety, 35(3), 220–228. https://doi.org/10.1002/da.22705.CrossRefGoogle ScholarPubMed
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., … Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X.CrossRefGoogle ScholarPubMed
Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., … Geddes, J. R. (2018). Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis. Lancet, 391(10128), 1357–1366. https://doi.org/10.1016/s0140-6736(17)32802-7.CrossRefGoogle ScholarPubMed
Crane, N. A., Jenkins, L. M., Bhaumik, R., Dion, C., Gowins, J. R., Mickey, B. J., … Langenecker, S. A. (2017). Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI. Brain, 140(2), 472–486. https://doi.org/10.1093/brain/aww326.CrossRefGoogle ScholarPubMed
Crown, W. H., Finkelstein, S., Berndt, E. R., Ling, D., Poret, A. W., Rush, A. J., & Russell, J. M. (2002). The impact of treatment-resistant depression on health care utilization and costs. Journal of Clinical Psychiatry, 63(11), 963–971. https://doi.org/10.4088/JCP.v63n1102.CrossRefGoogle ScholarPubMed
Delgadillo, J., & Salas Duhne, P. G. (2020). Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach. Journal of Consulting and Clinical Psychology, 88(1), 14–24. https://doi.org/10.1037/ccp0000476.CrossRefGoogle ScholarPubMed
Etkin, A., Patenaude, B., Song, Y. J. C., Usherwood, T., Rekshan, W., Schatzberg, A. F., … Williams, L. M. (2015). A cognitive-emotional biomarker for predicting remission with antidepressant medications: A report from the iSPOT-D trial. Neuropsychopharmacology, 40(6), 1332–1342. https://doi.org/10.1038/npp.2014.333.CrossRefGoogle ScholarPubMed
Faes, L., Liu, X., Wagner, S. K., Fu, D. J., Balaskas, K., Sim, D., … Denniston, A. K. (2020). A clinician's guide to artificial intelligence: How to critically appraise machine learning studies. Translational Vision Science and Technology, 9(2), 7. https://doi.org/10.1167/tvst.9.2.7.CrossRefGoogle ScholarPubMed
Fava, M. (2009). Partial responders to antidepressant treatment: Switching strategies. The Journal of Clinical Psychiatry, 70(7), e24. https://doi.org/10.4088/JCP.8017br3c.CrossRefGoogle ScholarPubMed
Fried, E. (2017). Moving forward: How depression heterogeneity hinders progress in treatment and research. Expert Review of Neurotherapeutics, 17(5), 423–425. https://doi.org/10.1080/14737175.2017.1307737.CrossRefGoogle ScholarPubMed
Gillan, C. M., & Whelan, R. (2017). What big data can do for treatment in psychiatry. Current Opinion in Behavioral Sciences, 18, 34–42. https://doi.org/10.1016/j.cobeha.2017.07.003.CrossRefGoogle Scholar
Guilloux, J. P., Bassi, S., Ding, Y., Walsh, C., Turecki, G., Tseng, G., … Sibille, E. (2015). Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacology, 40(3), 701–710. https://doi.org/10.1038/npp.2014.226.CrossRefGoogle ScholarPubMed
Guyon, I., Elisseeff, A., & Kaelbling, L. P. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(7-8), 1157–1182. https://doi.org/10.1162/153244303322753616.Google Scholar
Iniesta, R., Hodgson, K., Stahl, D., Malki, K., Maier, W., Rietschel, M., … Uher, R. (2018). Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Scientific Reports, 8(1), 5530. https://doi.org/10.1038/s41598-018-23584-z.CrossRefGoogle ScholarPubMed
Iniesta, R., Stahl, D., & McGuffin, P. (2016). Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine, 46(12), 2455–2465. https://doi.org/10.1017/S0033291716001367.CrossRefGoogle ScholarPubMed
James, G., Witten, D., Hastie, T., & Tibishirani, R. (2013). An introduction to statistical learning with applications in R. New York: Springer.CrossRefGoogle Scholar
Jaworska, N., De La Salle, S., Ibrahim, M. H., Blier, P., & Knott, V. (2019). Leveraging machine learning approaches for predicting antidepressant treatment response using electroencephalography (EEG) and clinical data. Frontiers in Psychiatry, 10, 768. https://doi.org/10.3389/fpsyt.2018.00768.CrossRefGoogle Scholar
Kambeitz, J., Goerigk, S., Gattaz, W., Falkai, P., Benseñor, I. M., Lotufo, P. A., … Brunoni, A. R. (2020). 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. Journal of Affective Disorders, 265, 460–467. https://doi.org/10.1016/j.jad.2020.01.118.CrossRefGoogle ScholarPubMed
Kautzky, A., Baldinger-Melich, P., Kranz, G. S., Vanicek, T., Souery, D., Montgomery, S., … Kasper, S. (2017). A new prediction model for evaluating treatment-resistant depression. Journal of Clinical Psychiatry, 78(2), 215–222. https://doi.org/10.4088/JCP.15m10381.CrossRefGoogle ScholarPubMed
Kennedy, S. H., Lam, R. W., McIntyre, R. S., Tourjman, S. V., Bhat, V., Blier, P., … Uher, R. (2016). Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 3. Pharmacological treatments. Canadian Journal of Psychiatry, 61(9), 540–560. https://doi.org/10.1177/0706743716659417.CrossRefGoogle ScholarPubMed
Kessler, R. C. (2018). The potential of predictive analytics to provide clinical decision support in depression treatment planning. Current Opinion in Psychiatry, 31(1), 32–39. https://doi.org/10.1097/YCO.0000000000000377.CrossRefGoogle ScholarPubMed
Kuhn, M., & Johnson, K. (2013). Over-fitting and model tuning. In Applied predictive modeling (pp. 61–89). New York: Springer. https://doi.org/10.1007/978-1-4614-6849-3.CrossRefGoogle Scholar
Lee, Y., Ragguett, R. M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., … McIntyre, R. S. (2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal of Affective Disorders, 241, 519–532. https://doi.org/10.1016/j.jad.2018.08.073.CrossRefGoogle ScholarPubMed
Lin, E., Kuo, P. H., Liu, Y. L., Yu, Y. W., Yang, A. C., & Tsai, S. J. (2018). A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Frontiers in Psychiatry, 9, 290. https://doi.org/10.3389/fpsyt.2018.00290.CrossRefGoogle ScholarPubMed
Luedtke, A., Sadikova, E., & Kessler, R. C. (2019). Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clinical Psychological Science, 7(3), 445–461. https://doi.org/10.1177/2167702618815466.CrossRefGoogle Scholar
Maciukiewicz, M., Marshe, V. S., Hauschild, A. C., Foster, J. A., Rotzinger, S., Kennedy, J. L., … Geraci, J. (2018). GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. Journal of Psychiatric Research, 99, 62–68. https://doi.org/10.1016/j.jpsychires.2017.12.009.CrossRefGoogle ScholarPubMed
Malone, D. C. (2007). A budget-impact and cost-effectiveness model for second-line treatment of major depression. Journal of Managed Care Pharmacy, 13(6 SUPPL. A), S8–18. https://doi.org/10.18553/jmcp.2007.13.s6-a.8.CrossRefGoogle ScholarPubMed
McGrath, C. L., Kelley, M. E., Holtzheimer, P. E., Dunlop, B. W., Craighead, W. E., Franco, A. R., … Mayberg, H. S. (2013). Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry, 70(8), 821–829. https://doi.org/10.1001/jamapsychiatry.2013.143.CrossRefGoogle Scholar
Milev, R. V., Giacobbe, P., Kennedy, S. H., Blumberger, D. M., Daskalakis, Z. J., Downar, J., … Ravindran, A. V. (2016). Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 4. Neurostimulation treatments. Canadian Journal of Psychiatry, 61(9), 561–575. https://doi.org/10.1177/0706743716660033.CrossRefGoogle ScholarPubMed
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.CrossRefGoogle ScholarPubMed
Nie, Z., Vairavan, S., Narayan, V. A., Ye, J., & Li, Q. S. (2018). Predictive modeling of treatment resistant depression using data from STARD and an independent clinical study. PLoS ONE, 13(6), e0197268. https://doi.org/10.1371/journal.pone.0197268.CrossRefGoogle Scholar
Oluboka, O. J., Katzman, M. A., Habert, J., McIntosh, D., MacQueen, G. M., Milev, R. V., … Blier, P. (2018). Functional recovery in major depressive disorder: Providing early optimal treatment for the individual patient. International Journal of Neuropsychopharmacology, 21(2), 128–144. https://doi.org/10.1093/ijnp/pyx081.CrossRefGoogle ScholarPubMed
Parikh, S. V., Quilty, L. C., Ravitz, P., Rosenbluth, M., Pavlova, B., Grigoriadis, S., … Uher, R. (2016). Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 2. Psychological treatments. Canadian Journal of Psychiatry, 61(9), 524–539. https://doi.org/10.1177/0706743716659418.CrossRefGoogle ScholarPubMed
Park, S. H., & Han, K. (2018). Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 286(3), 800–809. https://doi.org/10.1148/radiol.2017171920.CrossRefGoogle ScholarPubMed
Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F., & Aizenstein, H. J. (2015). Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. International Journal of Geriatric Psychiatry, 30(10), 1056–1067. https://doi.org/10.1002/gps.4262.CrossRefGoogle ScholarPubMed
Perlis, R. H. (2013). A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biological Psychiatry, 74(1), 7–14. https://doi.org/10.1016/j.biopsych.2012.12.007.CrossRefGoogle ScholarPubMed
Perlis, R. H. (2016). Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry, 15(3), 228–235. https://doi.org/10.1002/wps.20345.CrossRefGoogle ScholarPubMed
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., … Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163(11), 1905–1917. https://doi.org/10.1176/ajp.2006.163.11.1905.CrossRefGoogle ScholarPubMed
Samuel, A. L. (2000). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1.2), 207–219. https://doi.org/10.1147/rd.441.0206.CrossRefGoogle Scholar
Schmitt, P., Mandel, J., & Guedj, M. (2015). A comparison of six methods for missing data imputation. Journal of Biometrics & Biostatistics, 6(1), 1–6. https://doi.org/10.472/2155-6180.1000224.Google Scholar
Scott, I., Carter, S., & Coiera, E. (2021). Clinician checklist for assessing suitability of machine learning applications in healthcare. BMJ Health and Care Informatics, 28(1), e100251. https://doi.org/10.1136/bmjhci-2020-100251.CrossRefGoogle ScholarPubMed
Simon, G. E., & Perlis, R. H. (2010). Personalized medicine for depression: Can we match patients with treatments? American Journal of Psychiatry, 167(12), 1445–1455. https://doi.org/10.1176/appi.ajp.2010.09111680.CrossRefGoogle ScholarPubMed
Sinyor, M., Schaffer, A., & Levitt, A. (2010). The sequenced treatment alternatives to relieve depression (STAR*D) trial: A review. Canadian Journal of Psychiatry, 55(3), 126–135. https://doi.org/10.1177/070674371005500303.CrossRefGoogle ScholarPubMed
Tohka, J., & van Gils, M. (2021). Evaluation of machine learning algorithms for health and wellness applications: A tutorial. Computers in Biology and Medicine, 132, 104324. https://doi.org/10.1016/j.compbiomed.2021.104324.CrossRefGoogle ScholarPubMed
Trivedi, M. H., Rush, A. J., Wisniewski, S. R., Nierenberg, A. A., Warden, D., Ritz, L., … Fava, M. (2006). Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: Implications for clinical practice. American Journal of Psychiatry, 163(1), 28–40. https://doi.org/10.1176/appi.ajp.163.1.28.CrossRefGoogle ScholarPubMed
Uher, R., Frey, B. N., Quilty, L. C., Rotzinger, S., Blier, P., Foster, J. A., … Kennedy, S. H. (2020). Symptom dimension of interest-activity indicates need for aripiprazole augmentation of escitalopram in major depressive disorder: A CAN-BIND-1 report. Journal of Clinical Psychiatry, 81(4), e1–9. https://doi.org/10.4088/JCP.20m13229.Google ScholarPubMed
Uher, R., Perlis, R. H., Henigsberg, N., Zobel, A., Rietschel, M., Mors, O., … McGuffin, P. (2012a). Depression symptom dimensions as predictors of antidepressant treatment outcome: Replicable evidence for interest-activity symptoms. Psychological Medicine, 42(5), 967–980. https://doi.org/10.1017/S0033291711001905.CrossRefGoogle Scholar
Uher, R., Tansey, K. E., Malki, K., & Perlis, R. H. (2012b). Biomarkers predicting treatment outcome in depression: What is clinically significant? Pharmacogenomics, 13(2), 233–240. https://doi.org/10.2217/pgs.11.161.CrossRefGoogle Scholar
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14(11), e0224365. https://doi.org/10.1371/journal.pone.0224365.CrossRefGoogle ScholarPubMed
Vollmer, S., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P., … Hemingway, H. (2020). Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. The BMJ, 368, l6927. https://doi.org/10.1136/bmj.l6927.CrossRefGoogle Scholar
World Health Organization, . (2021). Depression. Retrieved from https://www.who.int/news-room/fact-sheets/detail/depression.Google Scholar
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., … Sullivan, P. F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681. https://doi.org/10.1038/s41588-018-0090-3.CrossRefGoogle ScholarPubMed
Yusuf, M., Atal, I., Li, J., Smith, P., Ravaud, P., Fergie, M., … Selfe, J. (2020). Reporting quality of studies using machine learning models for medical diagnosis: A systematic review. BMJ Open, 10(3), e034568. https://doi.org/10.1136/bmjopen-2019-034568.CrossRefGoogle ScholarPubMed
Zhang, Z. (2016). Missing data imputation: Focusing on single imputation. Annals of Translational Medicine, 4(1), 9. https://doi.org/10.3978/j.issn.2305-5839.2015.12.38.Google ScholarPubMed
Zisook, S., Lesser, I., Stewart, J. W., Wisniewski, S. R., Balasubramani, G. K., Fava, M., … Rush, A. J. (2007). Effect of age at onset on the course of major depressive disorder. American Journal of Psychiatry, 164(10), 1539–1546. https://doi.org/10.1176/appi.ajp.2007.06101757.CrossRefGoogle ScholarPubMed
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