Depression is estimated to affect more than 300 million people worldwide, and has a lifetime prevalence of 11.1% in Canada. In addition to the major toll that this depression takes on the lives of those affected and their families, the socioeconomic burden of the illness is enormous, costing $210.5 billion per year in the United States and $32.3 billion per year in Canada.
While a range of effective treatments for depression exist, these are not equivalently effective for all patients and some patients can spend years finding the right choice
This is time lost in the patients’ lives - time that is potentially away from work and when they are not able to be fully present in their families’ lives. Inadequately treated depression also leads to risks of suicide and self-harm.
This means that the decision about which treatment to try is one that has significant consequences. Aifred’s aim is to optimize treatment efficacy in psychiatry by leveraging a data-driven approach: making use of machine learning techniques along with a patient’s individual physiological profile to select the most effective interventions for that given patient’s depression. By training artificial neural networks on a vast quantity of naturalistic patient data, Aifred will learn to predict the best treatment or treatment cocktail at optimal dosages, while filtering out safety concerns such as drug-disease or drug-drug interactions.