Delivering Evidence Based Care using artificial Intelligence to Patients with Diabetes and CardioVascular Comorbidities: The DECIDE-CV Innovation Project

Many people who have Type 2 Diabetes Mellitus (T2DM) are never diagnosed until the development of a major health problem develops. Among people with T2DM with cardiovascular risk factors, evidence-based therapies can prevent future cardiovascular diseases from occurring. Artificial intelligence (A.I) represents an opportunity to address these major public health issues.

Design and fabrication of a wearable device (Watch HOP) for critical care, surgery and chronic disease monitoring

Diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose, which leads to serious damage to the heart, blood vessels, eyes, kidneys and nerves over time. It is estimated that around 10% of Canadians have diabetes, which is 1 of the 10 leading causes of death globally. While diabetes is highly prevalent, it is also seriously undiagnosed and mismanaged since only 50% of people with diabetes are aware that they have the condition.

Machine Learning in MEMS for Biomarkers Generation

The goal of the proposed project is to integrate the new AIMEMS sensor technology with wearable medical device to improve the generation of digital health biomarkers. AIMEMS form an entirely new class of MEMS, where AI capabilities are built directly into the dynamics of the MEMS. This tight integration of AI into physical devices allows the fabrication of compact and energy efficient sensors, that can be taught using standard machine learning algorithms to realize complex data filters, classifiers or other computational tasks.