Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
The NIPS '17 Competition: Building Intelligent Systems

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

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abadi M., et al. (2016). TensorFlow: Large–Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467v2

    Google Scholar 

  • American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders: DSM–IV–TR. Washington, DC: American Psychiatric Association.

    Google Scholar 

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC. Author[1]

    Google Scholar 

  • Asilomar AI Principles. 2016. Retrieved October 24 2017, from https://futureoflife.org/ai– principles/.

  • Beck A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck depression inventory–II. San Antonio, TX: Psychological Corporation.

    Google Scholar 

  • Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., Popp J. (2012). Sample Size Planning for Classification Models. arXiv:1211.1323 [stat.AP]

    Google Scholar 

  • Bergstra J., et al. 2010. “Theano: A CPU and GPU Math Compiler in Python”.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Braddock C. H. (2010). The Emerging Importance and Relevance of Shared Decision Making to Clinical Practice. Medical Decision Making, 30(5_ suppl), 5–7. https://doi.org/10.1177/0272989X10381344

    Article  Google Scholar 

  • Busner J., & Targum, S. D. (2007). The Clinical Global Impressions Scale. Psychiatry (Edgmont), 4(7), 28–37.

    Google Scholar 

  • 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. http://doi.org/10.2174/1570159X13666150307004545

    Article  Google Scholar 

  • Breitenstein B., Scheuer, S., Holsboer, F., 2014. Are there meaningful biomarkers of treatment response for depression? Drug Discov. Today 19, 539–61.

    Google Scholar 

  • 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

  • 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. https://doi.org/10.1097/PRS.0b013e318219c171

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/14651858.CD004366.pub6.

  • Cressey D. (2011). Psychopharmacology in crisis. Available at: https://www.nature.com/news/2011/110614/full/news.2011.367.html

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dice L. R. 1945. Measures of the amount of ecologic association between species. Ecology.; 26(3):297–302. https://doi.org/10.2307/1932409.

    Article  Google Scholar 

  • 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 

  • Dieleman S., et al. 2015. “Lasagne: First release.”

    Google Scholar 

  • 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 3313–3332

    MathSciNet  MATH  Google Scholar 

  • Duval F., Lebowitz, B.D., Macher, J.P., 2006. Treatments in depression. Dialogues in. Clin. Neurosci. 8, 191–206.

    Google Scholar 

  • 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. https://doi.org/10.1371/journal.pmed.1001547

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fushiki T. (2011). Estimation of prediction error by using K–fold cross–validation. Statistics and Computing, Volume 21, Issue 2, pp 137–146

    Article  MathSciNet  Google Scholar 

  • 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. https://doi.org/10.1192/bjp.180.2.101

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Goodfellow I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.

    MATH  Google Scholar 

  • Gravel R., Beland, Y. The Canadian Community Health Survey: mental health and well–being. Can J Psychiatry. 2005 Sep;50(10):573–9.

    Article  Google Scholar 

  • Guyon I., Elisseff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3 1157–1182

    MATH  Google Scholar 

  • Han J., Jentzen, A., Weinan, E. (2017). Overcoming the curse of dimensionality: Solving high–dimensional partial differential equations using deep learning. arXiv:1707.02568v1

    Google Scholar 

  • Hahn T., Nierenberg, A. A., & Whitfield–Gabrieli, S. (2017). Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Molecular psychiatry, 22(1), 37.

    Article  Google Scholar 

  • Hajian-Tilaki, K. 2013. “Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation”.

    Google Scholar 

  • 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 http://arxiv.org/abs/1704.00109

  • Hughes G. 2017. Montreal AI pioneer warns against unethical uses of new tech. CBC News.

    Google Scholar 

  • IEEE. 2016. The IEEE Global Initiative for Ethical Consideration in Artificial Intelligence and Autonomous Systems. Institute of Electrical and Electronics Engineers.

    Google Scholar 

  • Information Technology Industry Council. 2017. ITI AI Policy Principles. Retrieved from https://www.itic.org/resources/AI--Policy--Principles--FullReport2.pdf

    Google Scholar 

  • Intel. 2017. Artificial Intelligence? The Public Policy Opportunity. Intel Corporation. Retrieved from http://blogs.intel.com/policy/files/2017/10/Intel--Artificial--Intelligence--Public--Policy--White--Paper--2017.pdf

    Google Scholar 

  • Jolliffe IT. Principal Component Analysis. New York: Springer; 2002.

    MATH  Google Scholar 

  • 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. https://doi.org/10.1017/S1092852900017120

    Article  Google Scholar 

  • 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. http://www.nehi.net/writable/publication_files/file/cpg_report_final.pdf

  • 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. https://doi.org/10.1177/0706743716659417

    Article  Google Scholar 

  • 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. https://doi.org/10.1371/journal.pone.0041778

    Article  Google Scholar 

  • Kingma D. P., Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980v9

    Google Scholar 

  • Kirsch I. (2014). Antidepressants and the Placebo Effect. Zeitschrift Fur Psychologie, 222(3), 128–134. https://doi.org/10.1027/2151--2604/a000176

    Article  Google Scholar 

  • Klambauer G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). Self–Normalizing Neural Networks. arXiv:1706.02515 [Cs, Stat].

    Google Scholar 

  • Klengel T., Binder, E.B., 2013. Gene x environment interactions in the prediction of response to antidepressant treatment. Int. J. Neuropsychopharmacol. 16, 701–711

    Article  Google Scholar 

  • 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 

  • Kroenke K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ–9. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525--1497.2001.016009606.x

    Article  Google Scholar 

  • Lalkhen A. G., & McCluskey, A. (2008). Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain, 8(6), 221–223.

    Article  Google Scholar 

  • Lambert J. (2011). Statistics in Brief: How to Assess Bias in Clinical Studies? Clinical Orthopaedics and Related Research, 469(6), 1794–1796. https://doi.org/10.1007/s11999--010--1538--7

    Article  Google Scholar 

  • 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. https://doi.org/10.1111/nyas.12759

    Google Scholar 

  • 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. http://doi.org/10.1007/s11920--010--0160--4

  • 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 

  • 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, 29

    Google Scholar 

  • McIntyre R.S., 2010. When should you move beyond first–line therapy for depression? J. Clin. Psychiatry 71, 16–20.

    Article  Google Scholar 

  • Miller A.H., Haroon, E., Felger, J.C., 2016. Therapeutic Implications of Brain–Immune Interactions: Treatment in Translation. Neuropsychopharmacology 42, 334–359.

    Article  Google Scholar 

  • Montgomery S.A., Asberg M. (1979) A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389.

    Article  Google Scholar 

  • O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. (2016); Penguin Books

    Google Scholar 

  • 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 

  • Pasquale F., The Black Box Society: The Secret Algorithms That Control Money and Information (2015); Harvard University Press

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    MathSciNet  MATH  Google Scholar 

  • Pfizer (2018). https://www.pfizer.com/news/featured_stories/featured_stories_detail/learn_more_about_our_neuroscience_r_d_decision

  • 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.

    Article  Google Scholar 

  • Ronneberger O., Fischer, P., Brox, T. (2015). U–Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597

    Google Scholar 

  • Rosenfeld A., & Kraus S. (2018). Predicting Human Decision–Making: From Prediction to Action. Morgan and Claypool Publishing.

    Google Scholar 

  • 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 

  • 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:1506.06448v1

    Google Scholar 

  • 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 

  • Schmidt A. (2000). Implicit human computer interaction through context. Personal technologies, 4(2), 191–199.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shalev-Shwartz, S. (2012). Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.

    Article  Google Scholar 

  • Shin S. H., Bode, A. M., & Dong, Z. (2017). Precision medicine: the foundation of future cancer therapeutics. Npj Precision Oncology, 1(1), 12. https://doi.org/10.1038/s41698–017–0016–z

    Article  Google Scholar 

  • Simon G.E., Perlis, R.H., 2010. Personalized medicine for depression: Can we match patients with treatments? Am. J. Psychiatry 167, 1445–1455.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • Tai-Seale, M., McGuire, T. G., & Zhang, W. (2007). Time Allocation in Primary Care Office Visits. Health Services Research, 42(5), 1871–1894. https://doi.org/10.1111/j.1475–6773. 2006.00689.x

  • Taylor D., Paton, C., & Kapur, S. (2015). The maudsley prescribing guidelines in psychiatry.

    Google Scholar 

  • 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, Cham

    Chapter  Google Scholar 

  • Turecki G., & Brent, D. A. (2016). Suicide and suicidal behaviour. The Lancet, 387(10024), 1227–1239. https://doi.org/10.1016/S0140–6736(15)00234–2

    Article  Google Scholar 

  • van der Maaten L., Hinton G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html

    MATH  Google Scholar 

  • 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. https://doi.org/10.1176/appi.ajp.2017.16091059

    Article  Google Scholar 

  • Wallach W., & Allen, C. 2010. Moral Machines: Teaching Robots Right from Wrong. New York, NY, USA: Oxford University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. Geneva: World Health Organization; 2017.

    Google Scholar 

  • Xiao H., Rasul, K., Vollgraf, R. (2017) Fashion–MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747

    Google Scholar 

  • 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 

  • 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 

Download references

Author information

Authors and Affiliations

  1. Department of Psychiatry, McGill University, Montreal, QC, Canada

    David Benrimoh, Kelly Perlman, Nykan Mirchi, Sneha Desai, Ariel Rosenfeld, Sabrina Knappe, Jason Behrmann, Colleen Rollins & Raymond Penh You

  2. Wellcome Trust Center for Neuroimaging, University College London, London, UK

    David Benrimoh, Sneha Desai, Raymond Penh You & The Aifred Health Team

  3. Aifred Health, Montreal, QC, Canada

    David Benrimoh & Sneha Desai

  4. School of Computer Science, Montreal Neurological Institute, McGill University, Montreal, QC, Canada

    Robert Fratila & Ariel Rosenfeld

  5. Aifred Health, Montreal, QC, Canada

    Robert Fratila & Sabrina Knappe

  6. Department of Psychiatry, McGill University, Montreal, QC, Canada

    Sonia Israel & Sabrina Knappe

  7. Douglas Mental Health University Institute, Montreal, QC, Canada

    Sonia Israel & Jason Behrmann

  8. Aifred Health, Montreal, QC, Canada

    Sonia Israel & Colleen Rollins

  9. Department of Neurology & Neurosurgery, McGill University, Montreal, QC, Canada

    Kelly Perlman, Nykan Mirchi & Colleen Rollins

  10. Montreal Neurological Institute, Montreal, QC, Canada

    Kelly Perlman & Nykan Mirchi

  11. Aifred Health, Montreal, QC, Canada

    Kelly Perlman & Nykan Mirchi

  12. School of Computer Science, McGill University, Montreal, QC, Canada

    Sneha Desai

  13. Aifred Health, Montreal, QC, Canada

    Sneha Desai

  14. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Ariel Rosenfeld

  15. Department of Cognitive Science, McGill University, Montreal, QC, Canada

    Sabrina Knappe

  16. Aifred Health, Montreal, QC, Canada

    Sabrina Knappe

  17. Aifred Health, Montreal, QC, Canada

    Jason Behrmann

  18. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Colleen Rollins

  19. Aifred Health, Montreal, QC, Canada

    Colleen Rollins

  20. School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada

    Raymond Penh You

  21. Aifred Health, Montreal, QC, Canada

    Raymond Penh You

  22. The Aifred Health Team, Montreal, QC, Canada

    The Aifred Health Team

Authors
  1. David Benrimoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Fratila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sonia Israel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kelly Perlman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nykan Mirchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sneha Desai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ariel Rosenfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sabrina Knappe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jason Behrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Colleen Rollins
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Raymond Penh You
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. The Aifred Health Team
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Benrimoh .

Editor information

Editors and Affiliations

  1. Department Mathematics & Informatics, University of Barcelona, Barcelona, Spain

    Sergio Escalera

  2. Microsoft (United States), Redmond, WA, USA

    Markus Weimer

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Benrimoh, D. et al. (2018). Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health. In: Escalera, S., Weimer, M. (eds) The NIPS '17 Competition: Building Intelligent Systems. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-94042-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94042-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94041-0

  • Online ISBN: 978-3-319-94042-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation