Published 2018-07-06
Keywords
- depression,
- personalized medicine,
- artificial intelligence
How to Cite
Copyright (c) 2020 McGill Journal of Medicine

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Abstract
Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.
Downloads
References
- Fushiki T. Estimation of prediction error by using K-fold cross-validation. Statistics and Computing. 2011;21(2):137-46.
- Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). The Journal of clinical psychiatry. 2015;76(2):155-62.
- Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research 11 (2010) 3371-3408
- Günter Klambauer TU, Andreas Mayr, Sepp Hochreiter. Self-Normalizing Neural Networks. arXiv. 2017.
- Haffner YLLBYBP. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998;86(11):2278-324.
- Hajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian Journal of Internal Medicine. 2013;4(2):627-35.
- Karen Simonyan AV, Andrew Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv. 2013.
- Kennedy SH, Lam RW, McIntyre RS, Tourjman SV, Bhat V, Blier P, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 3. Pharmacological Treatments. Can J Psychiatry. 2016;61(9):540-60.
- Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
- Bengio, Y., Courville, A., & Vincent, P. (2012). Representation Learning: A Review and New Perspectives. arXiv:1206.5538 [Cs]. Retrieved from
- http://arxiv.org/abs/1206.5538
- SABCS 2016: IBM Watson for Oncology Platform Shows High Degree of Concordance With Physician Recommendations - The ASCO Post. (n.d.). Retrieved November 16, 2017, from http://www.ascopost.com/News/44214
- Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B.,
- Freedman, G., Murray, C. J. L., . . . Whiteford, H. A. (2013).
- Burden of Depressive Disorders by Country, Sex, Age, and
- Year: Findings from the Global Burden of Disease Study
- PLOS Medicine, 10(11), e1001547.
- doi:10.1371/journal.pmed.1001547
- Bromet, E., Andrade, L. H., Hwang, I., Sampson, N. A.,
- Alonso, J., de Girolamo, G., … Kessler, R. C. (2011).
- Cross-national epidemiology of DSM-IV major depressive
- episode. BMC Medicine, 9, 90.
- https://doi.org/10.1186/1741-7015-9-90
- Depression. (n.d.). Retrieved August 16, 2017, from
- http://www.who.int/mediacentre/factsheets/fs369/en/
- Kennedy, S. H., Lam, R. W., Parikh, S. V., MacQueen, G. M., Milev, R. V., Ravindran, A. V., & the, C. D.
- W. G. (2016). Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for
- the Management of Adults with Major Depressive Disorder: Introduction and Methods. Canadian Journal of
- Psychiatry. Revue Canadienne de Psychiatrie, 61(9), 506-509. doi:10.1177/0706743716659061
- Leuchter, A. F., Cook, I. A., Hunter, A. M., & Korb, A. S. (2009). A new paradigm for the prediction of
- antidepressant treatment response. Dialogues Clin Neurosci, 11(4), 435-446.
- Young, J. J., Silber, T., Bruno, D., Galatzer-Levy, I. R., Pomara, N., & Marmar, C. R. (2016). Is there
- Progress? An Overview of Selecting Biomarker Candidates for Major Depressive Disorder. Frontiers in
- Psychiatry, 7, 72. http://doi.org/10.3389/fpsyt.2016.00072
- Labermaier, C., Masana, M., & Müller, M. B. (2013). Biomarkers Predicting Antidepressant Treatment
- Response: How Can We Advance the Field? Disease markers, 35(1), 23-31. doi:10.1155/2013/984845
- Rush, A. J., Fava, M., Wisniewski, S. R., Lavori, P. W., Trivedi, M. H., Sackeim, H. A., . . . for the, S. D. I.
- G. (2004). Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Controlled
- Clinical Trials, 25(1), 119-142. doi:10.1016/S0197-2456(03)00112-0
- Maaten, L., Hinton, G. (2008). Visualizing Data using t-SNE. JMLR 9(Nov):2579--2605.
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural netoworks from overfitting. Journal of machine learning. 15(Jun):1929-1958