Ongoing projects

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Blockchain infrastructure for data donation using the Opal patient portal
Description
Description

This research project is focused on using blockchain or an alternative solution to provide security for data donation using the Opal app.

It will put in place a demonstrative blockchain infrastructure, examining its challenges and drawbacks and proposing potential innovative solutions.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Incorporating patient and clinician voices into social media associated with a patient portal
Description
Description

This project is centered on an examination of the process of preparing a patient-centered media and social media strategy that provides patients with useful information about the Opal patient portal and how they can make the most of it.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Asynchronous data federation for a multi-institutional patient portal
Description
Description

A patient portal is an extension of an electronic medical record system that is accessible to patients. Although patient portals have been around for many years, they have had poor adoption in Canada. This is due in large part to the desire of provinces to invest in large centralized electronic medical record systems and the complexity of implementing such systems. But patients are demanding access to their medical data and do not wish to wait for complex centralized systems to be implemented.

Therefore, in this research project, we will expand and evaluate the Opal patient portal, previously developed and implemented at the McGill University Health Centre, to function as a multi-institutional patient portal using a novel asynchronous data federation infrastructure.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Preparation of the Opal patient portal for widespread use at the Cedars Cancer Centre and for the addition of a caregiver component
Description
Description

This project involves two components: (1) preparation of Opal for the caregiver functionality in which patients will be able to share some or all of their medical data with their caregivers, and (2) general content preparation for Opal.

Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Empowered Patients, Informed Research - A pilot project for radiotherapy data donation using the Opal patient portal
Description
Description

This research project is focused on preparing a pilot project for the donation of radiotherapy data by radiotherapy patients using the Opal patient portal.

This project is investigating ways in which patients can share their data and it will put in place the infrastructure for a demonstrative project.

Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Privacy and confidentiality requirements for the use of a multi-institutional patient portal in Canada
Description
Description

This project involves an examination of the regulatory privacy and confidentiality compliance requirements for the use of a patient portal in various Canadian provinces.

Romina also works as a member of the quality assurance team, the market research team, and assisted with deploying Opal in numerous clinics within the Cedars Cancer Centre.

Directeur.e(s) de recherche
Jacques Corbeil
François Laviolette
Start date
Title of the research project
Development of an in-process quality monitoring technology for plants during pharmaceutical manufacturing using high throughput mass spectrometry coupled to machine learning approaches
Description
Description

Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

To this end, we must identify the factors that regulate the production level for each plant. We plan to measure a great deal of molecules, called metabolites, to determine the optimal conditions for the plants to generate the maximal amount of each product. Since the quantity of measurements is large, we will use machine learning to design an artificial intelligence capable of understanding and identifying the potentially highly complex patterns of metabolites and/or biomarkers that are correlated with the optimal production.

Student
Directeur.e(s) de recherche
Jacques Corbeil
François Laviolette
Start date
Title of the research project
Machine learning for digital diagnostics of antimicrobial resistance
Description
Description

The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach). New and improved approaches for tackling AMR include better surveillance; rational drug use, different business model for generating antibiotics, innovation at all levels and most importantly a global approach.

This transnational team grant proposal is tasked to apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. Specifically, we will develop machine learning implementation that can orient the selection of treatments by assessing the level of resistance, provide rational for the generation of novel antibiotics, and assist in the surveillance of human and livestock AMR around the globe.

To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills with demonstrated expertise in machine learning applied to both genomics and metabolomics and AMR domain experts. Our transnational team has all the elements to be highly impactful and to continue collaborating well past the JPIAMR funding period.

The complementarity of our expertise will help us to tackle the challenges ahead and ensure our continued success. 

Student
Directeur.e(s) de recherche
Philippe Després
Collaboration
Régie de l'assurance maladie du Québec
Start date
Title of the research project
Proof of concept for the development of a decision support tool allowing approval exceptional medications through artificial intelligence
Description
Description

An exception drug is a drug that is not usually covered by the public drug insurance plan (RPAM). The measures implemented at the RAMQ for exceptional drugs allow the entire population to obtain coverage for certain drugs if they are used in compliance with the indications recognized by the Institut national d'excellence en santé et services sociaux (INESSS). Exception drugs are now a large and constantly increasing part of total spending on prescription drugs.

For the RPAM, one of the ways to control this increase is to reimburse these drugs according to pre-established rules. Currently, the system automatically processes around 20% of requests while the rest are directed to a case-by-case analysis, which generates delays.

This project is to help the business sector respond more quickly to requests for approval of exception drugs. A tool will be developed based on 15 years of data collected by the current system, and will aim to increase the number of requests processed automatically.

Directeur.e(s) de recherche
Anne-Sophie Charest
Start date
Title of the research project
Statistical Analysis of Synthetic Data Sets Satisfying Differential Confidentiality
Description
Description

Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.

This project is interested in rigorously measuring the confidentiality protection offered by a synthetic dataset. We will carefully examine some measures proposed in the literature, to understand their guarantees and the differences and similarities between them in order to identify the measure (s) that would be the most relevant for the sharing of synthetic data.

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Featured project

Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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