Our tech team includes experienced machine learning engineers and researchers from McGill University and the Montreal Neurological Institute. We are constantly prototyping and refining newly published techniques to maintain state of the art methods. By working closely with both the product and research teams, we are able to implement critical domain knowledge in order to ensure a well-integrated product.
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. We believe we have the tools in place to get the most out of the data.
At aifred health, we strongly believe in the potential for artificial intelligence to augment medical practice. We aim to ease the integration of such technology into medical communities. Following this principle, every feature and product is developed upon a continuous consideration for physicians’ wants and needs. As a result, the aifred solution allows physicians to feel comfortable with the use of technology in their daily practice by combining innovative machine learning with a friendly user experience.
To identify an optimal profile of predictive biomarkers, we are reviewing the status of precision psychiatry in a variety of domains including genetics, endocrinology, immunology, metabolic biochemistry, and neuroimaging. This biological information combined with sociodemographic and clinical factors will yield insight on how to best implement the aifred solution.
Researchers have estimated that approximately one third of individuals suffering from depression do not recover following treatment1. Clinical research is focused on validating our model in controlled and real-world conditions. Firstly, the model must be user-friendly and provide clinicians with features they want and need. We must study the aifred solution’s integration into clinical workflow and any effects on clinician efficiency and doctor-patient interaction. Next the efficacy and effectiveness of the model must be assessed in open-label trials and against existing guidelines through randomized control trials. 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.
1. National Institute of Mental Health. Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study.
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. Simon Ducharme, MD, MSc, FRCPC - Neuroimaging Content Expert
Dr. Marcelo Berlim, MD, MSc - Literature Review and Neuromodulation Content Expert
Dr. Wendell Wallach - Bioethics and AI Ethics Content Expert
Tristan Sylvain - Machine Learning Content Expert
Dr. Margaux Luck - Machine Learning Content 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 - Special Populations (Women and Perinatal) 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
Dr. Jonathan Roiser, PhD - Computational Psychiatry Content Expert