Ongoing projects

Directeur.e(s) de recherche
Louis Archambault
Michèle Desjardins
Start date
Title of the research project
Effect of oxygen pressure in cancerous tissue cells on radiotherapy treatments
Description
Description

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
partial pressure in irradiated regions, particle type hitting the tissue, and treatment duration.

The Python code created as the main part of the project is intended to facilitate the optimization of radiotherapy treatment by generating graphs showing cell survival after a certain number of fractions, taking many parameters into account. When completed and integrated to a graphical interface, the code will be easy to use and helpful for ongoing research projects.

Student
Directeur.e(s) de recherche
Philippe Després
Anne-Sophie Charest
Start date
Title of the research project
Possibilities and limitations of DataSHIELD for health data privacy
Description
Description

It is often difficult to share denominalized data between different organizations and researchers due to ethical constraints related to respondent confidentiality. This is a common reality in the healthcare field, given the inherent sensitivity of this type of data. One option in this case is not to share the data directly, but rather to provide access to it via a tool that controls the risk of disclosure of the queries made and allows only those it considers safe. DataSHIELD is such a tool which has been proposed to protect the confidentiality of a dataset, and which can be used via the statistical software R. It also allows statistical analysis on several datasets hosted in different locations, always ensuring the confidentiality of the respondents. 

In this project, we are interested in the confidentiality guarantees provided by the software, and in its limitations. In particular, we wish to establish principles to guide the choice of disclosure control parameters offered with the tool, and to understand more precisely the impact of these controls on the quality of the descriptive statistics, linear models and graphs produced.
 

Student
Directeur.e(s) de recherche
François Laviolette
Jacques Corbeil
Alexandre Drouin
Start date
Title of the research project
Biomarkers discovery in high dimensional metabolomics with interpretable machine learning
Description
Description

Metabolomics is one way of studying metabolism. The presence of certain metabolites, or the breakdown of metabolic pathways can serve as indicators of a patient's health. They can serve as markers for certain diseases such as cancers, or provide information on the quality of an individual's diet. Untargeted metabolomics acquisition methods produce large data matrices. The aim is to develop machine learning methods specifically suited to handle high dimensional data sets. For example models based on decision rules.
The purpose of these models being the search for biomarkers, they must be sparse in order to be able to be interpreted by a human expert. We also try to develop new approaches to better interpret some existing and efficient models. Interpretability is essential in the application of machine learning to health. Models cannot be diagnostic black boxes but rather analytical tools available to experts to better understand human metabolism. 

Directeur.e(s) de recherche
Nadia Lahrichi
Start date
Title of the research project
Intraoperative analgesic treatment decisions based on the NOL index: contribution of a data-based approach to improve accuracy and relevance
Description
Description

This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 
The second step is to develop an artificial intelligence algorithm that can guide decision-making for greater precision and better anesthetic safety for the patient.
 

Directeur.e(s) de recherche
Vicky Drapeau
Yves De Koninck
Philippe Després
Collaboration
PULSAR
Start date
Title of the research project
Design, operationalization and validation of a sustainable health evaluation model adapted to a digital platform
Description
Description

The project focuses on the design, operationalization and validation of a sustainable health evaluation model.
This model will be adapted to a digital platform and based on solid theoretical and conceptual foundations. Furthermore, it will gather valid indicators and will be supplied by data showing a global and ecosystem conception of health.
Once operationalized, implemented and validated in a cohort study, this model will represent an innovative strategy for sustainable health through improved technologies and intervention methods.
 

Directeur.e(s) de recherche
François Laviolette
Start date
Title of the research project
Full characterization of drug-drug interactions using deep learning approaches
Description
Description

The characterization of Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs
co-administration. While considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The joint impact of the majority of combinations remains undetected until therapies are prescribed to patients. This raises the need for computational tools predicting DDIs in order to reduce experimental costs and exhaustively characterize all drug combination effects before therapy recommendations. 
Previous attempts to build such tools focused on pharmacodynamic and pharmacokinetic interactions and used features that are difficult to access in the early stages of R&D. 
In this work, we propose to use data about the drugs and their targets (pathways, biomarkers, gene expressions, etc) that are available at the beginning of each drug R&D campaign. Our hypothesis is that high-level deep learning features extracted from those data will improve DDI characterization. Therefore, our models will be trained to output the pharmacological effects of DDIs as well as underlying molecular and biological pathway interactions. 
Creating such a comprehensive toolkit will help to reduce risks in polypharmacy therapies.
 

Directeur.e(s) de recherche
Philippe Després
Guillaume Latzko-Toth
Start date
Title of the research project
Pseudo-medicine and data science: impact study of learning algorithms in the propagation of misinformation in health field
Description
Description

This project studies the consequences of artificial intelligence (AI) systems and data science on public discourse, as well as their usage by the new content providers on the Web. 
It will tackle the ethical aspects of learning algorithms and recommendation filters implemented by internet companies to select and present content to the user. Specifically, the project investigates the consequences of such algorithms on public health, especially in the propagation of medical misinformation and pseudo-medicine.

This project aims at taking a critical oversight on data science techniques and their use. Various knowledge from different fields of humanities and social science will be applied (ethics, communication studies, philosophy of techniques) and will guide the development of technical solutions, as well as recommendations for the implementation of ethical and sustainable AI. 
For this reason, we will need both technical and philosophical research, working towards interdisciplinary integration.
 

Student
Directeur.e(s) de recherche
Louis Archambault
Simon Duchesne
Start date
Title of the research project
Hetero-modal synthesis of medical images using deep learning
Description
Description

The research project is focused on the synthesis of medical images using deep learning, towards better artifact correction and the avoidance of unnecessary medical procedures.

The neural networks designed in this project have a flexible architecture enabling the image synthesis from only an heterogeneous subset of input modalities. The images are synthesized in pathological situations, such as Alzheimer's disease and brain cancers.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Preparing a patient portal for use as a self-management tool
Description
Description

This project is part of the effort to prepare Opal for use by patients, providing them with self-management resources such as questionnaires and educational materials.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Undertaking a Privacy Impact Assessment for a novel data donation platform based on the Opal patient portal
Description
Description

This project is centered on the data donation aspect of Opal and will involve a Privacy Impact Assessment of the application and the eventual practice of data sharing driven by it.

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Featured project

The aim of this doctoral thesis is to develop a tool able to automatically provide organs of interest segmentation in computed tomography images using machine learning techniques.

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