Using machine learning to increase treatment efficacy in mental health.

Meet the Team

Discover how we are going to change mental health!

aifred health is all about helping clinicians predict which treatments will get the best results with the fewest side effects, so that clinicians and patients can have more information when deciding on a treatment plan together. Our solution is a clinical decision aid that can be used by clinicians to input patient features and output personalized lists of treatments that include predicted efficacy and side-effect profile.
We are also a proud official IBM Watson AI XPRIZE team, headquartered in Montreal, Canada.
Depression is a serious mental illness that globally affects more than 300 million people. Some patients can spend years finding the right treatment regimen for them, and many patients won’t improve after the first treatment. The inability to predict any given individual’s unique response to medical treatment is a huge bottleneck to recovery.
To address this challenge, we are using a deep learning approach to build a predictive model based on the best available data.
Initially, we will be focusing on treatments for depression, but we plan to scale aifred to encompass all mental health conditions in order to amplify clinical utility. At its core, aifred is leveraging the collective intelligence of the scientific and medical community to bring better healthcare to all.

Read more about us:

Deep Learning

Something unique to every machine learning company is the precise nature of their hyperparameter optimization and goals of their model. We will optimize aifred with the help of a distributed network of domain experts in psychiatry--a collaboration unique to aifred health. We are implementing attention networks responsible for removing the “black-box” nature of neural networks. As well, we are analysing the quality of model predictions, allowing both for greater interpretability of model decisions and the generation of new basic research questions, which are going to be unique to the data-set and optimization techniques we develop in-house. By training aifred on reliable datasets, we are able to ensure quality input to our model. De-identified patient outcomes will feed back into our neural networks to continuously improve aifred’s predictive power. Feature engineering is an important part of determining which inputs go into a network and varies how it’s done for every team- once again, this will be undertaken with the support of diverse group of experts we are recruiting.

Our Product

We offer much more than treatment prediction!

Treatment Prediction

Work with our ai system to generate personalized treatment predictions for your patients.


Forget the blackbox! our system will provide a report explaining the important features that led to a treatment prediction.

Patient Data Tracking

Track patient symptoms or other information to monitor outcomes or make new predictions.

Electronic Patient Record

Keep all important patient information in one place, and get insights using our analytics. Delighters like a calendar and to-do list help keep you on-task.

From Fundamental to Clinical


Our research team is conducting a series of systematic literature reviews to curate predictors of treatment response and side effect burden in depression. We are evaluating the state of precision psychiatry in domains including genetics, endocrinology, immunology, metabolic biochemistry, and neuroimaging, as well as examining the feasibility of including biomarker testing in routine clinical practice. The results of these reviews will serve to validate our model and inform the input feature space by integrating these multimodal biomarkers along with sociodemographic and clinical factors.


Clinical research is focused on validating our model in controlled and real-world conditions. We are designing three kinds of research trials indicated below. Safety is critical, so our clinical team, which includes two physicians, will be making sure to review our model’s predictions and ensure hard-coded safety features so that model treatment recommendations are safe. We are blazing the trail when it comes to clinical validation of deep-learning based clinical decision aids, and as such are investing heavily in the development of ethical principles to guide development and testing. In fact ethical development is so important to us that we have created our own ethical framework, known as Meticulous Transparency, to guide our work. We also never store identifiable patient information, to protect patient privacy.


The model must be user-friendly and provide clinicians with features they want and need, so we must study the aifred solution’s integration into clinical workflow and any effects on clinician efficiency and doctor-patient interaction.

Open Label Trials

Safety and effectiveness of the model must be assessed in open-label trials where both clinicians and patients know when our model is being used. A group of physicians using our model will be compared to a group practicing usual care, and patient outcomes will be compared between the two.

Randomized Control Trial

After open label studies, we will conduct one or more randomized control trials, testing our model against a “dummy” model and against a “practice as usual” group. This will help us determine how efficacious the aifred solution is.

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Dr. Gustavo Turecki, MD, PhD - Genetics, Dataset Access Content Expert
Dr. Marc Miresco, MD, MSc - External Psychiatry Services Content Expert
Dr. Leon Tourian, MD - Medical Education Content Expert
Dr. Thomas Milroy, MD - Electroconvulsive Therapy Content Expert
Dr. Gail Myhr, MDCM, Dip Psy, MSc, FRCP - Cognitive Behavioral Therapy Content Expert
Dr. Eduardo Chachamovitch, MD, PhD - Psychometrics, Mood disorders and Special Populations Content Expert
Dr. Marcelo Berlim, MD, MSc - Literature Review and Neuromodulation Content Expert
Dr. Howard Margolese, MD - Clinical Trial Expert

Dr. Daniel Blumberger, MD, MSc, FRCPC - rTMS and Neuromodulation Content Expert
Dr. Sagar Parikh, MD - Guidelines, Best Practices, Cultural Safety Content Expert
Dr. Simone Vigod, MD, MSc, FRCPC - Guidelines, Best Practices, Cultural Safety Content Expert
Dr. Anthony J. Levitt, MD, MBBS, FCPC - Depression Treatment Optimization Content Expert
Dr. Roger S. McIntyre, MD, FRCPC - Psychiatry and Neuropharmacology Content Expert

Tristan Sylvain - Machine Learning Content Expert
Dr. Margaux Luck - Machine Learning Content Expert

Dr. Wendell Wallach - Bioethics and AI Ethics Content Expert

Dr. Jonathan Roiser, PhD - Computational Psychiatry Content Expert

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