Opioid-related harms such as abuse, misuse, addiction, diversion and overdose have been rising exponentially, a phenomenon referred to as the opioid epidemic. The current research will examine federal and provincial risk minimization measures (RMMs) regarding the opioid epidemic starting in 2016. We will develop a landscape of federal and provincial opioid RMMs, describe trends over time in the number and types of RMMs, assess the association between RMMs and public awareness and the association between RMMs and opioid-related harms.
This project seeks to explore the use of a class of artificial intelligence algorithms called reinforcement learning for the purpose of aiding the training of new pilots. In the process, we seek to “teach” an algorithm how to fly an aircraft by exposing the AI pilot to a virtual environment and providing it with flight data and a goal. Alternatively, the algorithm could learn by observing human pilots.
We’d like to address the issue of 3D reconstruction from 2D images. This means developing a machine learning algorithm that can take a regular photo as an input and generate a full 3-dimensional reconstruction of the contents of the photo. Such technology can be used creatively or to help the coming generation of robots better understand their surroundings.
Leukemia, lymphoma and other forms of blood cancers are still largely diagnosed every year in Canada. These diseases constitute the second leading cause of cancer related death in young adults and the sixth in adult. The five-year survival rates still range between 42% and 85%. Currently, the main treatment is a stem cell transplantation which unfortunately do not prevent lethal relapse. The goal of this study is to develop and improve a novel cellular therapy aiming to limit and prevent relapse of hematological malignancies.
Autism spectrum disorder refers to a broad range of conditions characterized by challenges with social skills, repetitive behaviours, speech, and nonverbal communication. This once misunderstood disorder affects a broad range of communications skills and behaviours.
According to Public Health Agency Canada, one in 66 Canadian children are diagnosed with autism.
Patients with corneal disease often require treatment with scleral lenses. Unlike regular soft contact lenses, these lenses are much larger and have a space between the cornea and the lens that is filled with fluid before lens application. These lenses are extremely useful in cases of extremely ocular dryness and in patients with irregular corneas. Adjusting these lenses to perfectly mold the surface of the eye is of the utmost importance to ensure that the patient is comfortable and sees well with their lenses.
As the urgency for action against climate change increases, local governments around the world are committing to reducing greenhouse gas emissions through deep decarbonization targets. Cities are the largest place-based sources of GHG emissions and therefore have great potential to reduce emissions on a global scale.
The main duty of Hydro-Québec is to repond efficiently to the energy demand of customers, in a safe and secure way while remaining competitive in the markets as well. The main goal of this start-up project is to support Hydro-Québec in developing a future-oriented energy system by proposing innovative technical solutions. Among these solutions, deep learning has been the final choice. Using a deep learning approach, satellite images, weather model outputs and data from solar radiation measurement stations, will be use for the development of a solar radiation nowcasting model.
Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations.
ClosedLoop.ai is an AI-based predictive analytics platform that goes beyond traditional claims-based risk scores to use all patient-related healthcare data to provide both clinicians and care managers with a full breadth of timely, transparent and accurate predictions of health outcomes. ClosedLoop.ai helps value-based providers confidently answer a variety of health-care questions like, which patients are most likely to be readmitted to the hospital? Or which of my patients would most benefit from establishing a relationship with a primary care provider?