References
- Anderson, J. W., Greenway, F. L., Fujioka, K., Gadde, K. M., McKenney, J., & O’Neil, P. M. (2002). Bupropion SR enhances weight loss: A 48-week double-blind, placebo-controlled trial. Obesity Research, 10(7), 633–641. DOI: https://doi.org/10.1038/oby.2002.86, PMID: 12105285
- Candès, E., Fan, Y., Janson, L., & Lv, J. (2018). Panning for gold: “Model-X” knockoffs for high-dimensional controlled variable selection. Journal of the Royal Statistical Society, Series B, 80(3), 551–557. DOI: https://doi.org/10.11 11/rssb.12265
- Carter, G. C., Cantrell, R. A., Zarotsky, V., Haynes, V. S., Phillips, G., Alatorre, C. I., Goetz, I., Paczkowski, R., & Marangell, L. B. (2012). Comprehensive review of factors implicated in the heterogeneity of response in depression. Depression and Anxiety, 29(4), 340–354. DOI: https://doi.org/10.1002/da.21918, PMID: 22511365
- Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatry, 3(3), 243–250. DOI: https://doi.org/10.1016/S2215-0366(15)00471-X
- DeRubeis, R. J., Cohen, Z. D., Forand, N. R., Fournier, J. C., Gelfand, L. A., & Lorenzo-Luaces, L. (2014). The Personalized Advantage Index: Translating research on prediction into individualized treatment recommendations—a demonstration. PLoS ONE, 9, e83875. DOI: https://doi.org/10.1371/journal.pone.0083875, PMID: 24416178, PMCID: PMC3885521
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press. https://www.deeplearningbook.org/
- He, H., & Garcia, E. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. DOI: https://doi.org/10.1109/TKDE.2008.239
- 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, 5530. DOI: https://doi.org/10.1038/s41598-018-23584-z, PMID: 29615645, PMCID: PMC5882876
- Iniesta, R., Malki, K., Maier, W., Rietschel, M., Mors, O., Hauser, J., … Uher, R. (2016). Combining clinical variables to optimize prediction of antidepressant treatment outcomes. Journal of Psychiatric Research, 78, 94–102. DOI: https://doi.org/10.1016/j.jpsychires.2016.03.016, PMID: 27089522
- Kapelner, A., Bleich, J., Levine, A., Cohen, Z. D., DeRubeis, R., & Berk, R. (2020). Evaluating the effectiveness of personalized medicine with software. arXiv:1404.7844v3.
- Kennedy, S. H., Lam, R. W., McIntyre, R. S., Tourjman, S. V., Bhat, V., Blier, P., … CANMET Depression Work Group. (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. DOI: https://doi.org/10.1177/0706743716659061, PMID: 27486152, PMCID: PMC4994787
- Keogh, E., & Mueen, A. (2017). Curse of dimensionality. In C. Sammut & G. I. Webb (Eds.), Boston, MA: Springer. DOI: https://doi.org/10.1007/978-1-4899-7687-1_192
- Kessler, R. C. (2012). The costs of depression. Psychiatric Clinics of North America, 35(1), 1–14. DOI: https://doi.org/10.1016/j.psc.2011.11.005, PMID: 22370487, PMCID: PMC3292769
- Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980.
- Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). Self-normalizing neural networks. arXiv:1706.02515.
- Lang, K., Liberty,E., & Shmakov,K. (2016). Stratified sampling meets machine learning. In Proceedings of the 33rd International Conference on Machine Learning (Vol. 48, pp. 2320–2329). New York, NY: JMLR.org.
- Lee, Y., Ragguett, R.-M., Mansur, R. B., Boutiliere, J. J., Rosenblat, J. D., Trevizol, A., … McIntyre, R. S. (2019). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal of Affective Disorders, 241, 519–532. DOI: https://doi.org/10.1016/j.jad.2018.08.073, PMID: 30153635
- L’Heureux, A., Grolinger, K., & Capretz, M. A. M. (2017). Machine learning with big data: Challenges and approaches. New York, NY: IEEE. DOI: https://doi.org/10.1109/ACCESS.2017.2696365
- Lin, E., Kuo, P.-H., Liu, Y.-L., Yu, Y. W.-Y., 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. DOI: https://doi.org/10.3389/fpsyt.2018.00290, PMID: 30034349, PMCID: PMC6043864
- Patel, K., Allen, S., Haque, M. N., Angelescu, I., Baumeister, D., & Tracy, D. K. (2016). Bupropion: A systematic review and meta-analysis of effectiveness as an antidepressant. Therapeutic Advances in Psychopharmacology, 6, 99–144. DOI: https://doi.org/10.1177/2045125316629071, PMID: 27141292, PMCID: PMC4837968
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
- Perlman, K., Benrimoh, D., Israel, S., Rollins, C., Brown, E., Tunteng, J.-F., … Berlim, M. T. (2019). A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. Journal of Affective Disorders, 243, 503–515. DOI: https://doi.org/10.1016/j.jad.2018.09.067, PMID: 30286415
- Rosenblatt, F. (1957). The perceptron—a perceiving and recognizing automaton (Report No. 85-460-1). Ithaca, NY: Cornell Aeronautical Laboratory.
- Rush, A. J., Trivedi, M. H., Stewart, J. W., Nierenberg, A. A., Fava, M., Kurian, B. T., … Wisniewski, S. R. (2011). Combining medications to enhance depression outcomes (CO-MED): Acute and long-term outcomes of a single-blind randomized study. American Journal of Psychiatry, 168(7), 689–701. DOI: https://doi.org/10.1176/appi.ajp.2011.10111645, PMID: 21536692
- 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. DOI: https://doi.org/10.1176/ajp.2006.163.11.1905, PMID: 17074942
- Saint Onge, J. M., Krueger, P. M., & Rogers, R. G. (2014). The relationship between major depression and nonsuicide mortality for U.S. adults: The importance of health behaviors. Journals of Gerontology, Series B, 69(4), 622–632. DOI: https://doi.org/10.1093/geronb/gbu009, PMID: 24569003, PMCID: PMC4049146
- Samek, W., Wiegand, T., & Müller, K.-R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv:1708.08296.
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
- Stewart, W. F., Ricci, J. A., Chee, E., Hahn, S. R., & Morganstein, D. (2003). Cost of lost productive work time among US workers with depression. JAMA, 289(23), 3135–3144. DOI: https://doi.org/10.1001/jama.289.23.3135, PMID: 12813119
- 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. DOI: https://doi.org/10.1176/appi.ajp.163.1.28, PMID: 16390886
- Turecki, G., & Brent, D. A. (2016). Suicide and suicidal behaviour. Lancet, 387(10024), 1227–1239. DOI: https://doi.org/10.1016/S0140-6736(15)00234-2
- Uher, R. (2011). Genes, environment, and individual differences in responding to treatment for depression. Harvard Review of Psychiatry, 19(3), 109–124. DOI: https://doi.org/10.3109/10673229.2011.586551, PMID: 21631158
- World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. Geneva, Switzerland: Author.
