Emerging Event Classification System

The goal is to develop a system that can rapidly detect and report emerging disease outbreaks worldwide by analyzing clusters of news articles using Large Language Models. The objective is to create an efficient and effective way of identifying "disease
events" that can alert public health officials to take prompt action.

Using AI to inform Doctors in a Cardiology Platform

Cardiac specialists spend anywhere from 60-70% of their patient consult working with electronic medical record (EMR) software attempting to extract salient patient data that leads to providing the most accurate diagnosis and care path. Cadea Health Inc. is seeking to reduce the workload cardiologist face by taking advantage of AI to intelligently gather and present relevant information. Cardea’s platform is seeking to utilize AI to extract non-standardized patient data from multiple sources into a coherent data flow and single source.

Designing an Augmented Immersive Virtual Reality Driving Simulator for Advanced Alzheimer’s Disease Patients and Investigating its efficacy on institutionalized Alzheimer’s Residents

In this project a driving simulator in virtual reality will be designed and developed, in which a user can drive a virtual vehicle in a country road with incoming cars and traffic lights and possibly some animals crossing the road. The users will learn the path to reach a destination through the trial and then they are supposed to drive the virtual vehicle in the same pathway and by doing so, strengthen their spatial navigation skills. The game will be played by a physical steering wheel and two pedals for acceleration and brake like a real car.

Deep learning-based classification and survival prediction of pediatric brain tumors using digital pathology images

The research project aims to improve the classification and diagnosis of a type of brain tumour known as pediatric low-grade gliomas (PLGGs) using advanced computer technology and the study of how the tumour looks under a microscope. By doing this, physicians can better understand what causes the tumours and develop new treatments that are more effective. The research will lead to better outcomes for young patients with PLGGs and improve their quality of life.

A Machine Learning Framework for Exploring Mortality in Developing Countries with Verbal Autopsies

This research project, backed by Unity Health Toronto and the Centre for Global Health Research (CGHR), aims to explore the use of machine learning in predicting causes of death using verbal autopsy data from low-to-middle-income countries. Verbal autopsy is a cost-effective and efficient method for documenting deaths in regions with limited resources.

QT-Prolonging Medications and Major Adverse Cardiac Events

Some medications affect the QT interval of the heartbeat and can result in major heart events, which can lead to death. It is unclear exactly how much medications compared to other risk factors contribute to these outcomes and when they will occur. This research project will use data from St. Joseph’s Healthcare Hamilton’s (SJHH) Dovetale-Epic electronic medical record (EMR). Patients who were or were not taking medications that affect the heartbeat and experienced serious heart events will be identified.

A complementary paper microfluidics and sample preservation approach for on-site and in-lab assessment of semen quality

We propose to the develop a process whereby a semen sample can be initially assessed for sperm motility on-site
followed by preservation of the remaining sample for transfer to a lab for quantitative sperm quality analyses. A
microfluidic device using paper will be developed for the on-site motility test, whereby the device shows a colorimetric
signal visible to the eye correlating to the percentage of motile sperm from a few drops of semen.

A feasibility and acceptability study of implementing a Collaborative service robot in long-term care

Service robots can offer personalized service for people with disabilities and empower the staff with efficient support. This study investigates using a collaborative service robot, Aether in a long-term care home. We work collaboratively with people living with disabilities, families, frontline staff, operation leaders, and industry) to Identify user experience, impact and challenges to inform future robot development and adoption. The study will be conducted through three phases: (1) Plan, (2) Adapt, and (3) Evaluate.

Peptide-based material for heart muscle repair

With over 17 million deaths per year, heart diseases remain the top cause of mortality worldwide. Surgeries such as bypass restore blood supply and save lives. However, after a heart attack the capacity of the heart to pump blood is reduced. In Canada, over 2 million people aged 20+ live with heart disease, costing the healthcare system $2.8+ billion and thousands die each year. Approximately 1 in 4 of these patients develop heart failure, a number that increases by ~50,000/year. For many of those patients, a heart transplant is the only option.

Personalized event recommendation system in community-based healthcare

Social isolation and loneliness are serious public health issues with many adverse effects on individuals' quality of life and well-being. Notably, these are growing problems in the aging population. Working from an existing prototype and extension of our previous Mitacs project, the main objective of this research project is to develop and optimize a personalized sequential recommendation system to address an individual’s flagged unmet needs or to advance identified personal goals based on their survey data in order to improve their quality of life and overall satisfaction.

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