Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health

Conference paper
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)


Aifred Health, one of the top two teams in the first round of the IBM Watson AI XPRIZE competition, is using deep learning to solve the problem of treatment selection and prognosis prediction in mental health, starting with depression. Globally, depression affects over 300 million people and is the leading cause of disability. While a range of effective treatments do exist, patients’ responses to treatments vary to a large degree. Some patients spend years going through a frustrating ‘trial-and-error’ process in order to find an effective treatment. The Aifred Health solution is a deep learning-powered Clinical Decision Support System (CDSS) aimed at helping clinicians select the most effective treatment plans for depression in collaboration with their patients. In this chapter, we discuss problem of treatment selection in depression and explore the technical, clinical, and ethical dimensions of building a CDSS for mental health based on deep learning technology.


Clinical Decision Support Systems (CDSS) Deep Learning Canadian Network For Mood And Anxiety Treatments (CANMAT) Transcranial Direct-current Stimulation Montgomery-Asberg Depression Rating Scale (MADRS) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abadi M., et al. (2016). TensorFlow: Large–Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467v2Google Scholar
  2. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders: DSM–IV–TR. Washington, DC: American Psychiatric Association.Google Scholar
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC. Author[1]Google Scholar
  4. Asilomar AI Principles. 2016. Retrieved October 24 2017, from– principles/.
  5. Beck A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck depression inventory–II. San Antonio, TX: Psychological Corporation.Google Scholar
  6. Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., Popp J. (2012). Sample Size Planning for Classification Models. arXiv:1211.1323 [stat.AP]Google Scholar
  7. Bergstra J., et al. 2010. “Theano: A CPU and GPU Math Compiler in Python”.Google Scholar
  8. Berlim M.T., Fleck, M.P., Turecki, G., 2008. Current trends in the assessment and somatic treatment of resistant refractory major depression: An overview. Ann. Med. 40, 149–159.CrossRefGoogle Scholar
  9. Berlim M.T., Turecki, G., 2007. Definition, assessment, and staging of treatment–resistant refractory major depression: A review of current concepts and methods. Can. J. Psychiatry 52, 46–54.CrossRefGoogle Scholar
  10. Braddock C. H. (2010). The Emerging Importance and Relevance of Shared Decision Making to Clinical Practice. Medical Decision Making, 30(5_ suppl), 5–7. CrossRefGoogle Scholar
  11. Busner J., & Targum, S. D. (2007). The Clinical Global Impressions Scale. Psychiatry (Edgmont), 4(7), 28–37.Google Scholar
  12. Brand S. J., Möller, M., & Harvey, B. H. (2015). A Review of Biomarkers in Mood and Psychotic Disorders: A Dissection of Clinical vs. Preclinical Correlates. Current Neuropharmacology, 13(3), 324–368. CrossRefGoogle Scholar
  13. Breitenstein B., Scheuer, S., Holsboer, F., 2014. Are there meaningful biomarkers of treatment response for depression? Drug Discov. Today 19, 539–61.Google Scholar
  14. 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.
  15. Burns P. B., Rohrich, R. J., & Chung, K. C. (2011). The Levels of Evidence and their role in Evidence–Based Medicine. Plastic and Reconstructive Surgery, 128(1), 305–310. CrossRefGoogle Scholar
  16. Chi K.F., Korgaonkar, M., Grieve, S.M., 2015. Imaging predictors of remission to anti–depressant medications in major depressive disorder. J. Affect. Disord. 186, 134–144.CrossRefGoogle Scholar
  17. Cooney GM, Dwan K, Greig CA, Lawlor DA, Rimer J, Waugh FR, McMurdo M, Mead GE. Exercise for depression. Cochrane Database of Systematic Reviews 2013, Issue 9. Art. No.: CD004366.
  18. Cressey D. (2011). Psychopharmacology in crisis. Available at: Google Scholar
  19. De Carlo, V., Calati, R., Serretti, A., 2016. Socio–demographic and clinical predictors of non–response/non–remission in treatment resistant depressed patients: A systematic review. Psychiatry Res. 240, 421–430.CrossRefGoogle Scholar
  20. Dice L. R. 1945. Measures of the amount of ecologic association between species. Ecology.; 26(3):297–302. Scholar
  21. Dichter G.S., Gibbs, D., Smoski, M.J., 2016. A systematic review of relations between resting–state functional–MRI and treatment response in major depressive disorder. J. Affect. Disord. 172.Google Scholar
  22. Dieleman S., et al. 2015. “Lasagne: First release.”Google Scholar
  23. Dmochowski J. P., Sajda, P., Parra, L. C. (2010). Maximum Likelihood in Cost–Sensitive Learning: Model Specification, Approximations, and Upper Bounds. Journal of Machine Learning Research 11 3313–3332MathSciNetzbMATHGoogle Scholar
  24. Duval F., Lebowitz, B.D., Macher, J.P., 2006. Treatments in depression. Dialogues in. Clin. Neurosci. 8, 191–206.Google Scholar
  25. 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 2010. PLOS Medicine, 10 (11), e1001547. CrossRefGoogle Scholar
  26. Fitzpatrick K. K., Darcy, A., & Vierhile, M. (2017). Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Mental Health, 4(2), e19.CrossRefGoogle Scholar
  27. Fushiki T. (2011). Estimation of prediction error by using K–fold cross–validation. Statistics and Computing, Volume 21, Issue 2, pp 137–146MathSciNetCrossRefGoogle Scholar
  28. Gilbody S. M., House, A. O., & Sheldon, T. A. (2002). Psychiatrists in the UK do not use outcomes measures: National survey. The British Journal of Psychiatry, 180(2), 101–103. CrossRefGoogle Scholar
  29. Goldman HH, Skodol AE, Lave TR: “Revising Axis V for DSM–IV: A Review of Measures of Social Functioning.” American Journal of Psychiatry 149:1148–1156, 1992.CrossRefGoogle Scholar
  30. Goodfellow I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.zbMATHGoogle Scholar
  31. Gravel R., Beland, Y. The Canadian Community Health Survey: mental health and well–being. Can J Psychiatry. 2005 Sep;50(10):573–9.CrossRefGoogle Scholar
  32. Guyon I., Elisseff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3 1157–1182zbMATHGoogle Scholar
  33. Han J., Jentzen, A., Weinan, E. (2017). Overcoming the curse of dimensionality: Solving high–dimensional partial differential equations using deep learning. arXiv:1707.02568v1Google Scholar
  34. Hahn T., Nierenberg, A. A., & Whitfield–Gabrieli, S. (2017). Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Molecular psychiatry, 22(1), 37.CrossRefGoogle Scholar
  35. Hajian-Tilaki, K. 2013. “Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation”.Google Scholar
  36. Huang G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E., Weinberger, K.Q.(2017). Snapshot Ensembles: Train 1, get M for free. arXiv: 1704.00109v1 [Cs, Stat] . Retrieved from
  37. Hughes G. 2017. Montreal AI pioneer warns against unethical uses of new tech. CBC News.Google Scholar
  38. IEEE. 2016. The IEEE Global Initiative for Ethical Consideration in Artificial Intelligence and Autonomous Systems. Institute of Electrical and Electronics Engineers.Google Scholar
  39. Information Technology Industry Council. 2017. ITI AI Policy Principles. Retrieved from Google Scholar
  40. Intel. 2017. Artificial Intelligence? The Public Policy Opportunity. Intel Corporation. Retrieved from Google Scholar
  41. Jolliffe IT. Principal Component Analysis. New York: Springer; 2002.zbMATHGoogle Scholar
  42. Kemp A., Gordon, E., Rush, A., & Williams, L. (2008). Improving the Prediction of Treatment Response in Depression: Integration of Clinical, Cognitive, Psychophysiological, Neuroimaging, and Genetic Measures. CNS Spectrums, 13(12), 1066–1086. CrossRefGoogle Scholar
  43. Kenefick H., Lee J., Fleishman V. (2008). Improving Physician Adherence to Clinical Practice Guidelines, Barriers and strategies for change, New England Healthcare Institute, February 2008.
  44. Kennedy S. H., Lam, R. W., McIntyre, R. S., Tourjman, S. V., Bhat, V., Blier, P., et al. CANMAT 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. Revue Canadienne De Psychiatrie, 61(9), 540–560. CrossRefGoogle Scholar
  45. Khan A., Faucett, J., Lichtenberg, P., Kirsch, I., & Brown, W. A. (2012). A Systematic Review of Comparative Efficacy of Treatments and Controls for Depression. Plos One, 7(7), e41778. CrossRefGoogle Scholar
  46. Kingma D. P., Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980v9Google Scholar
  47. Kirsch I. (2014). Antidepressants and the Placebo Effect. Zeitschrift Fur Psychologie, 222(3), 128–134. CrossRefGoogle Scholar
  48. Klambauer G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). Self–Normalizing Neural Networks. arXiv:1706.02515 [Cs, Stat].Google Scholar
  49. Klengel T., Binder, E.B., 2013. Gene x environment interactions in the prediction of response to antidepressant treatment. Int. J. Neuropsychopharmacol. 16, 701–711CrossRefGoogle Scholar
  50. Krizhevsky A., Sutskever, I., Hinton, G. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (NIPS 2012)Google Scholar
  51. Kroenke K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ–9. Journal of General Internal Medicine, 16(9), 606–613. CrossRefGoogle Scholar
  52. Lalkhen A. G., & McCluskey, A. (2008). Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain, 8(6), 221–223.CrossRefGoogle Scholar
  53. Lambert J. (2011). Statistics in Brief: How to Assess Bias in Clinical Studies? Clinical Orthopaedics and Related Research, 469(6), 1794–1796. CrossRefGoogle Scholar
  54. Lener M.S., Iosifescu, D. V, 2015. In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Google Scholar
  55. Leuchter A. F., Cook, I. A., Hamilton, S. P., Narr, K. L., Toga, A., Hunter, A. M., . . . Lebowitz, B. D. (2010). Biomarkers to predict antidepressant response. Curr. Psychiatry Rep. 12, 553–562.
  56. Lopresti A.L., Maker, G.L., Hood, S.D., Drummond, P.D., 2013. A review of peripheral biomarkers in major depression: The potential of inflammatory and oxidative stress biomarkers, in: Progress in Neuro Psychopharmacology and Biological Psychiatry. 48.Google Scholar
  57. Luo W., Li, Y., Urtason, R., Zemel, R. (2016). Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 29Google Scholar
  58. McIntyre R.S., 2010. When should you move beyond first–line therapy for depression? J. Clin. Psychiatry 71, 16–20.CrossRefGoogle Scholar
  59. Miller A.H., Haroon, E., Felger, J.C., 2016. Therapeutic Implications of Brain–Immune Interactions: Treatment in Translation. Neuropsychopharmacology 42, 334–359.CrossRefGoogle Scholar
  60. Montgomery S.A., Asberg M. (1979) A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389.CrossRefGoogle Scholar
  61. O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. (2016); Penguin BooksGoogle Scholar
  62. Papakostas G.I., Fava, M., 2009. Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder. Dialogues Clin. Neurosci. 10, 439–451.Google Scholar
  63. Pasquale F., The Black Box Society: The Secret Algorithms That Control Money and Information (2015); Harvard University PressGoogle Scholar
  64. Patel V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M. R., Bellazzi, R., & Abu–Hanna, A. (2009). The coming of age of artificial intelligence in medicine. Artificial intelligence in medicine, 46(1), 5–17.CrossRefGoogle Scholar
  65. Pedregosa F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. 2011. Scikit–learn: Machine Learning in Python. The Journal of Machine Learning Research. Volume 12, pages 2825–2830.MathSciNetzbMATHGoogle Scholar
  66. Porcelli S., Fabbri, C., Serretti, A., 2012. Meta–analysis of serotonin transporter gene promoter polymorphism (5–HTTLPR) association with antidepressant efficacy. Eur. Neuropsychopharmacol. 22, 239–258.CrossRefGoogle Scholar
  67. Ronneberger O., Fischer, P., Brox, T. (2015). U–Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597Google Scholar
  68. Rosenfeld A., & Kraus S. (2018). Predicting Human Decision–Making: From Prediction to Action. Morgan and Claypool Publishing.Google Scholar
  69. Rosenfeld A., Keshet, J., Goldman, C. V., & Kraus, S. (2016). Online Prediction of Exponential Decay Time Series with Human–Agent Application. In ECAI (pp. 595–603).Google Scholar
  70. Roth H., Lu, L., Farag, A., Shin, A.C., Liu, J., Turkbey, E., Summers, R. (2015). DeepOrgan: Multi–level Deep Convolutional Networks for Automated Pancreas Segmentation. Arxiv:1506.06448v1Google Scholar
  71. Sansone R. A., & Sansone, L. A. (2012). Antidepressant Adherence: Are Patients Taking Their Medications? Innovations in Clinical Neuroscience, 9(5–6), 41–46.Google Scholar
  72. Schmidt A. (2000). Implicit human computer interaction through context. Personal technologies, 4(2), 191–199.CrossRefGoogle Scholar
  73. Schmidt F.M., Kirkby, K.C., Lichtblau, N., 2016. Inflammation and immune regulation as potential drug targets in antidepressant treatment. Curr. Neuropharmacol. 14, 674–687.CrossRefGoogle Scholar
  74. Shalev-Shwartz, S. (2012). Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.CrossRefGoogle Scholar
  75. Shin S. H., Bode, A. M., & Dong, Z. (2017). Precision medicine: the foundation of future cancer therapeutics. Npj Precision Oncology, 1(1), 12.–017–0016–zCrossRefGoogle Scholar
  76. Simon G.E., Perlis, R.H., 2010. Personalized medicine for depression: Can we match patients with treatments? Am. J. Psychiatry 167, 1445–1455.CrossRefGoogle Scholar
  77. Simonyan K., Vedaldi, A., Zisserman, A. (2013). Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv:1312.6034v2 [Cs, Stat].Google Scholar
  78. Srivastava N., Hinton, G., Krizhevsky, A., Ilya Sutskever, I., Salakhutdinov, R. (2013). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15 (1929–1958)Google Scholar
  79. Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Karmar E., Kraus S., Leyton–Brown K., Parkes D., Press W., Sanexian A., Shah J., Tambe M., Teller A. (2016). Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence.Google Scholar
  80. Tai-Seale, M., McGuire, T. G., & Zhang, W. (2007). Time Allocation in Primary Care Office Visits. Health Services Research, 42(5), 1871–1894.–6773. 2006.00689.x
  81. Taylor D., Paton, C., & Kapur, S. (2015). The maudsley prescribing guidelines in psychiatry.Google Scholar
  82. Thum F. et al. (2014) Usability Improvement of a Clinical Decision Support System. In: Marcus A. (eds) Design, User Experience, and Usability. User Experience Design for Everyday Life Applications and Services. DUXU 2014. Lecture Notes in Computer Science, vol 8519. Springer, ChamCrossRefGoogle Scholar
  83. Turecki G., & Brent, D. A. (2016). Suicide and suicidal behaviour. The Lancet, 387(10024), 1227–1239.–6736(15)00234–2CrossRefGoogle Scholar
  84. van der Maaten L., Hinton G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605. zbMATHGoogle Scholar
  85. Walkup J. T. (2017). Antidepressant Efficacy for Depression in Children and Adolescents: Industry– and NIMH–Funded Studies. American Journal of Psychiatry, 174(5), 430–437. CrossRefGoogle Scholar
  86. Wallach W., & Allen, C. 2010. Moral Machines: Teaching Robots Right from Wrong. New York, NY, USA: Oxford University Press.Google Scholar
  87. Warden D., Rush, A.J., Trivedi, M.H., Fava, M., Wisniewski, S.R., 2008. The STAR*D Project results: a comprehensive review of findings. Curr Psychiatry Rep 9, 449–459.CrossRefGoogle Scholar
  88. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. Geneva: World Health Organization; 2017.Google Scholar
  89. Xiao H., Rasul, K., Vollgraf, R. (2017) Fashion–MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747Google Scholar
  90. Yoon J., Alaa, A., Hu, S., & Schaar, M. (2016). ForecastICU: a prognostic decision support system for timely prediction of intensive care unit admission. In International Conference on Machine Learning (pp. 1680–1689).Google Scholar
  91. 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.Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PsychiatryMcGill UniversityMontrealCanada
  2. 2.Wellcome Trust Center for NeuroimagingUniversity College LondonLondonUK
  3. 3.Aifred HealthMontrealCanada
  4. 4.School of Computer Science, Montreal Neurological InstituteMcGill UniversityMontrealCanada
  5. 5.Aifred HealthMontrealCanada
  6. 6.Department of PsychiatryMcGill UniversityMontrealCanada
  7. 7.Douglas Mental Health University InstituteMontrealCanada
  8. 8.Aifred HealthMontrealCanada
  9. 9.Department of Neurology & NeurosurgeryMcGill UniversityMontrealCanada
  10. 10.Montreal Neurological InstituteMontrealCanada
  11. 11.Aifred HealthMontrealCanada
  12. 12.School of Computer ScienceMcGill UniversityMontrealCanada
  13. 13.Aifred HealthMontrealCanada
  14. 14.Department of Computer Science and Applied MathematicsWeizmann Institute of ScienceRehovotIsrael
  15. 15.Department of Cognitive ScienceMcGill UniversityMontrealCanada
  16. 16.Aifred HealthMontrealCanada
  17. 17.Aifred HealthMontrealCanada
  18. 18.Department of PsychiatryUniversity of CambridgeCambridgeUK
  19. 19.Aifred HealthMontrealCanada
  20. 20.School of Physical and Occupational TherapyMcGill UniversityMontrealCanada
  21. 21.Aifred HealthMontrealCanada
  22. 22.The Aifred Health TeamMontrealCanada

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