Aifred Health

Bringing science and tech together

Aifred’s focus is using real machine learning, on real data, in a novel combination.

High quality clinical datasets are used as data input: responses to surveys from patients participating in clinical trials testing different medications. Our solution does not need expensive or time-consuming tests, therefore we can easily introduce the tool to clinics and scale rapidly. This helps us improve care quality while reducing costs. A global problem needs a global solution, building localized versions of our AI model will enable us to meet this global need.

Our published research

We have created collaborative research partnerships in Canada, UK, US and Israel to further our research and are committed to the principles open innovation in the areas of basic science and the rigor of peer review processes.

Our technology and approach is also disease-area agnostic. It can be adapted and applied beyond depression in the future, to address many global diseases where treatment selection is a challenge.

We rigorously test our models to ensure we don’t propagate biases, and we are implementing and pioneering new approaches to model interpretability, both of which are part of our commitment to AI ethics.

AI Ethics (2)

  • Benrimoh, D., Israel, S., Perlman, K., Fratila, R., & Krause, M. (2018). Meticulous Transparency —An Evaluation Process for an Agile AI Regulatory Scheme . In M. Mouhoub, S. Sadaoui, O. Ait Mohamed, & M. Ali (Eds.), Recent Trends and Future Technology in Applied Intelligence (pp. 869 –880). Springer International Publishing.

  • A new role for the private sector in responsible innovation. David E. Winickoff , Sebastian M. Pfotenhauer , Nina Frahm, David Benrimoh, Hermann Garden, Judy Iles, Thomas R. Insel, Gary E. Marchant (In press, Nature Biotechnology)

Psychiatry (2)

  • Perlman, K., Benrimoh, D., Israel, S., Rollins, C., Brown, E., Tunteng, J.F., You, R., You, E., Tanguay-Sela, M., Snook, E. and Miresco, M., 2019. A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. Journal of affective disorders, 243, pp.503-515.

  • Snook, E., Perlman, K., Brown, E.H., Langlois-Therien, T., Benrimoh, D., Tanguay-Sela, M., Rollins, C., You, E. and Berlim, M.T., 2021. A systematic meta-review of predictors of adverse effect development in response to antidepressant medications. University of Toronto Medical Journal, 98(2).

AI (7)

  • Benrimoh, D., Tanguay-Sela, M., Perlman, K., Israel, S., Mehltretter, J., Armstrong, C., ... Margolese, H. (2021). Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician–patient interaction. BJPsych Open, 7(1), E22.

  • Mehltretter, J., Fratila, R., Benrimoh, D. A., Kapelner, A., Perlman, K., Snook, E., … Turecki, G. (2020). Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data. Computational Psychiatry, 4, 61–75.

  • Tanguay-Sela, M., Benrimoh, D., Perlman, K., Israel, S., Mehltretter, J., Armstrong, C., Fratila, R., Parikh, S., Karp, J., Heller, K. and Vahia, I., 2020. Evaluating the Usability and Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment. Biological Psychiatry, 87(9), p.S171.

  • Desai S, Tanguay-Sela M, Benrimoh D, Fratila R, Brown E, Perlman K, John A, Del Pozo Baños M, Low N, Israel S, Palladini L, Turecki G. (2019). Prediction of Suicidal Ideation in the Canadian Community Health Survey - Mental Health Component Using Deep Learning. Pre-print.

  • Mehltretter J, Rollins C, Benrimoh D, Perlman K, Fratila R, Israel S, Wakid M, Miresco M, Turecki G. (2019). Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression. Frontiers in Artificial Intelligence, 2, 31.

  • Rosenfeld, A., Benrimoh, D., Armstrong, C., Mirchi, N., Langlois-Therrien, T., Rollins, C., Tanguay-Sela, M., Mehltretter, J., Fratila, R., Israel, S. and Snook, E., 2021. Big Data analytics and artificial intelligence in mental healthcare. In Applications of Big Data in Healthcare (pp. 137-171). Academic Press.

  • Benrimoh D., Fratila R., Israel S., Perlman K., Mirchi N., Rosenfeld A., Knappe S., Behrmann J., Rollins C., You RP. (2018). ‘Aifred Health’, a Deep Learning-Powered Clinical Decision Support System for Mental Health. The Springer Series on Challenges in Machine Learning. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017) Competition Track.