Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data
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.
DOI: https://doi.org/10.1162/cpsy_a_00029 | Journal eISSN: 2379-6227
Language: English
Submitted on: May 16, 2019
Accepted on: Oct 19, 2020
Published on: Jan 1, 2020
Published by: MIT Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2020 Joseph Mehltretter, Robert Fratila, David A. Benrimoh, Adam Kapelner, Kelly Perlman, Emily Snook, Sonia Israel, Caitrin Armstrong, Marc Miresco, Gustavo Turecki, published by MIT Press
This work is licensed under the Creative Commons Attribution 4.0 License.