Ongoing projects

Directeur.e(s) de recherche
Louis Archambault
Start date
Title of the research project
Implementation of interpretation techniques on a neural network for prognosis prediction for prostate cancer
Description
Description

As tools derived from artificial intelligence are used more frequently in medicine and health-related domains, understanding their predictions becomes increasingly important when determining the trustworthiness of a prediction. 
As the goal of this project is to interpret a neural network, it requires two main phases: the evaluation of the model and the interpretation of the model. The success of the first phase required the implementation of generalised methods to evaluate neural networks according to pre-established metrics. Once the model's quality is determined through the first phase, it is possible to implement the interpretation techniques that allow a human user to understand and analyse the model's predictions, thus concluding the second phase of the project.
The project's third and final phase was comprised of the analysis of the interpretation data obtained from the new methods and the presentation of the results to the other people working on this same neural network.
 

Directeur.e(s) de recherche
Elsa Rousseau
Start date
Title of the research project
Study of bacterial-phage interactions in the intestinal microbiota of the general population of Quebec
Description
Description

Alexandre Boulay's project involves the analysis of phages and bacteria in the gut microbiota from a metagenomic dataset from the Institute of Nutrition and Functional Foods (INAF) at Université Laval, relying on bioinformatics and artificial intelligence (AI) methods. The dataset comes from a recent study that examined the interaction of the endocannabinoid axis with host environmental factors as well as gut, metabolic and mental health status in Quebec adults with various metabolic and lifestyle statuses. The overall objective is to train interpretable AI algorithms to identify phage and bacterial biomarkers of metabolic and mental health in individuals, and to study the interactions between bacteria and phage. This project could have important implications for the understanding of the interactions between bacteria and phages, which are very poorly known, but also for the knowledge of the gut microbiota of the Quebec population in relation to their metabolic and mental health.

Directeur.e(s) de recherche
Elsa Rousseau
Start date
Title of the research project
Study of bacterial-phage interactions in in vitro gut digestion systems
Description
Description

Rose-Marie's project focuses on the analysis of interactions between bacteriophages - the viruses of bacteria - and bacteria of the intestinal microbiota based on datasets from experiments carried out by the student in collaboration with members of the Institute of Nutrition and Functional Foods (INAF) at Université Laval. The first objective is to study the impact of phages on bacterial dynamics in a simplified microbiota, composed of 8 key bacterial strains of the human intestinal microbiota. The second objective is to study the bacterial-phage dynamics in a complex microbiota representative of the human gut microbiota. For both objectives, following the experimentation and acquisition of sequencing data, Rose-Marie will perform data analysis using bioinformatics methods. This project could have important implications for the understanding of interactions between bacteria and phages, which are very poorly known, but also for the knowledge of the intestinal microbiota in relation to nutrition and health of individuals.

Directeur.e(s) de recherche
Philippe Després
Start date
Title of the research project
Conversion of HDF5 Segmentation Files to DICOM
Description
Description

The intern developed a tool for converting pulmonary nodule annotation data stored inHDF5 files to theDICOMfile format. The tool enables the extraction of annotation data from the HDF5 file as well as the lung computed tomography (CT) data of patients stored in a database. Subsequently, the tool generates and saves a DICOM annotation file following the structure indicated by the DICOM Standard Browser. The student programmed this tool in Python while keeping track of versions using Gitlab. The intern’s project facilitated the conversion of data for around a hundred patients.

Ultimately, the tool can be reused by other members of the research group in the future for projects requiring annotation data conversion to DICOM

Directeur.e(s) de recherche
Simon Duchesne
Start date
Title of the research project
Computational model of cerebral aging bridging nano, micro, and mesoscales
Description
Description

One of the primary challenges of diagnosing Alzheimer’s Disease (AD) lies in its progression through two silent decades. The lack of symptoms in patients during this time evidently hinders their chance of suspecting the disease, or merely being granted a precautionary brain scan. Moreover, the initial endogenous signs and noticeable symptoms often coincide with aging individuals without any neurological disease diagnosis. In the midst of these diagnostic challenges, fundamental and clinical research efforts have provided a sea of disparate information about the pathophysiology by tracing back the events that unfold with respect to small and large scale components. To capture the multifactorial nature of AD in the face of a heavily delayed diagnostic timeframe, it becomes intractable to attempt to account for the abundance of causal candidate factors for AD using standard analytical statistical techniques. 
Instead of tracing back AD signs and symptoms, we aim to simulate normal aging going forward in time, in the hopes of detecting more accurate early Alzheimer’s signs as they emerge, and subsequently diverge from typical aging-associated abnormalities. Therefore, we propose to restructure AD knowledge into several levels of abstraction or scales with the reliance on mathematical modeling techniques to represent AD more comprehensively, while inspiring the model from the process of normal aging from 18- to 100-year-old humans. 
We will conduct a thorough literature search to estimate parametric values required to satisfy our system of ordinary and partial differential equations, tailored to simulate normal aging. We will use an Agile approach to categorize entities known to play a role in aging such as 1) at the nanoscale with compounds like glucose and insulin, and proteins such as amyloid and tau; 2) at the microscale based on neuronal and glial populations as well as the vascular endothelium; 3) bringing them together to simulate and predict the trajectory of biomarkers at the mesoscale (e.g., neuronal integrity via cortical thickness, metabolic integrity via FDG-PET). The model’s use of estimated theoretical parameter values will in turn be validated with human data to orient its development in concordance with the longitudinal trajectory of the aging human.  
The multiscale hierarchy of neurological diseases which is composed of an incredibly complex interactome alarmingly prompt us to move on from single-component analyses, towards more carefully dissecting the most impactful entities, while adequately accounting for how they intertwine with each other during aging. This framework can provide a starting point for earlier detection of AD neurodegeneration and potentially facilitate the identification of more AD-specific pathways for future pharmacological interventions. 
 

Directeur.e(s) de recherche
Christian Gagné
Start date
Title of the research project
Confidentiality-preserving synthetic data generation from administrative healthcare databases
Description
Description

Synthetic healthcare datasets are useful to support the development of data analysis and machine learning techniques in healthcare, by offering access to representative data to experiment and generate models from while mitigating the issues associated with dealing with highly sensitive data related to human subjects. However, the performance and usefulness of data analysis and machine learning methods applied depend on the quality of these synthetic datasets and their representativity of the phenomenon to model.
The objective of the project is to develop machine learning methods for generating synthetic healthcare datasets that preserve the distribution and the temporality of real administrative healthcare datasets while ensuring that the confidentiality of sensitive information on persons found in the real dataset is preserved. This means to have some guarantees that the capacity to identify real people from the original dataset is not possible or very unlikely, and that attributes of the real records (e.g. personal healthcare history) can not be inferred from the synthetic dataset. Depending on the guarantees we can get in ensuring the confidentiality over the real open medical data used in generating the synthetic datasets, it would be considered to produce synthetic versions of RAMQ datasets, and even to disclose them more openly for research and analysis purposes if that is deemed to be acceptable.
 

Student
Directeur.e(s) de recherche
Anne-Sophie Charest
Start date
Title of the research project
Confidentiality guarantees of a new method to generate synthetic data
Description
Description

It is often difficult, even sometimes impossible, to share denominalized data between organisations and researchers due to ethical constraints regarding participant confidentiality. Synthetic datasets could facilitate data sharing. However, many current methods, which use multiple imputation (MI) techniques for missing data, lower the analysis potential and the quality of the results.

This project therefore aims to assess the confidentialy guarantees of a promising new data synthesis method. This method adds a data masking step to a multiple imputation technique to generate synthetic data based on the risk of each observation. In particular, attribute disclosure risks, which refer to the disclosure of certain attributes based on other, known ones, will be tested.

The feasibility and quality of the results will be tesed on a dataset provided by l’Institut de la statistique du Québec.
 

Directeur.e(s) de recherche
France Légaré
Start date
Title of the research project
Sustainability of health professionals' intention to have serious illness conversations about advance care planning at 1 and 2 years after training: a cluster randomized trial
Description
Description

While some studies report the positive effects of continuing professional development (CPD) on clinical behaviour, few address the sustainability of these effects as well as the types of approaches that could improve this sustainability.

Our aim was to compare the durability of healthcare professionals' intention to have conversations with patients in cases of serious illness, after training using an interprofessional approach or an individual approach. We conducted a cluster randomised clinical trial with measurements immediately (T1), at 1 year (T2) and at 2 years (T3) after the intervention in primary care clinics in Canada and the United States. Results are reported according to CONSERVE (2021) guidelines. Clinics were randomly assigned to either interprofessional team training (intervention) or individual training (comparator). Our primary outcome of interest, healthcare professionals' intention to have conversations in cases of serious illness, and associated psychosocial variables (social norm, moral norm, beliefs about consequences, and beliefs about abilities) were measured using the CPD-Reaction questionnaire. Data were collected using self-administered questionnaires at 3 stages after training (T1, T2 and T3). Bivariate and multivariate statistical analyses were performed using a linear mixed model for each study time with an interaction term between time and arm. The average age of the 373 participants was 35-44 years, and 79% were women. On a scale of 1 to 7, at T1 the mean intention was 6.0 (SD 1.12) for the interprofessional arm and 6.4 (SD 0.7) for the individual arm. At T2, it was 5.65 (SD 1.39) and 6.04 (SD 0.88) in the interprofessional and individual arms respectively. At T3, it was 5.5 (SD 1.53) in the interprofessional arm and 6.3 (SD 0.74) in the individual arm. The p-value for the interaction between study arm and time was 0.05. The difference in mean intention between the two study arms was 0.02 (CI -0.26 to 0.31), -0.07 (CI -0.49 to 0.34), -0.55 (-1.00 to -0.10) at T1, T2 and T3 respectively. At T3, it was 5.5 (SD 1.53) in the interprofessional arm and 6.3 (SD 0.74) in the individual arm. The p-value for the interaction between study arm and time was 0.05. The difference in mean intention between the two study arms was 0.02 (CI -0.26 to 0.31), -0.07 (CI -0.49 to 0.34), -0.55 (-1.00 to -0.10) at T1, T2 and T3 respectively. In conclusion, healthcare professionals' intention to have conversations in cases of serious illness varied over time according to the training approach. This intention was lower at the 1- and 2-year follow-up after training using an interprofessional approach compared with training using an individual approach.

Our results could help to improve continuing professional development, and hence the quality of care offered.

Directeur.e(s) de recherche
Elsa Rousseau
Start date
Title of the research project
Development of a tool for the prediction of phage’s bacterial host by machine learning
Description
Description

Mariame Gnéré Coulibaly's project will focus on the development of a tool for the prediction of bacterial host of phages by machine learning (ML). The first objective will be to develop a ML tool to identify bacteria-phage pairs from CRISPR spacer sequences (clustered regularly interspaced short palindromic repeats) found in bacterial genomes, which are fragments of phage genomes that have infected the bacteria. The second objective is to develop a ML tool to identify bacteria-phage pairs based on sequence and methylation information (i.e. the addition of methyl groups to nucleotides). For the first two objectives, algorithms such as neural networks with attention mechanisms, and similarity predictors based on string kernels, will be developed and tested.

The third objective is to develop a multi-view algorithm combining the two previous objectives, with one view for CRISPR information and a second view for methylation patterns.

The tools developed will have a strong impact on the microbiology and virology research community, by being able to identify new bacteria-phage pairs from microbiota samples.

Directeur.e(s) de recherche
France Légaré
Samira Abbasgholizadeh-Rahimi
Start date
Title of the research project
Assessing the Intention of Pregnant Women and/or their Partners to Use a Mobile Application-Based Decision Support Tool for Prenatal Screening
Description
Description

Introduction: Chromosomal disorders such as trisomy 21 (most common), 18 and 13 are a source of concern for parents, in terms of fetal health, delivery/miscarriage; this may be compounded by concerns about financial issues. These concerns increase with the age of the mother (1,2). Prenatal screening is used to assess the likelihood of having a fetus with such anomaly(ies) and if necessary further diagnostic tests. Previous studies have shown that pregnant women actively seek information to make informed decisions about testing: what options are available? Are they prepared enough to make a choice? Do they have all the necessary information? How can their own values and preferences be considered?

Effective decision support tools exist to help people facing difficult decisions to make informed choices (4). However, the evidence contained in these tools has not been evaluated based on the relative weight it contributes to the decision to be made. Furthermore, the intentions of pregnant women and their partners to use such a tool in a digital format are unknown (5).

 

Objective: The main objective of our study is to assess the intention of pregnant women and/or their partners to use a mobile application-based decision support tool for prenatal screening. More specifically, it will: identify potential factors that may influence decision making; assess the relative weight of the information contained in the tool ascribed by pregnant women and/or their partners; and assess their intention to use it.

 

Methods: Study design: This is a descriptive cross-sectional study that represents phase 1 of a project called APP4WE (Analytical mobile application to support shared decision making for pregnant women) of the Canada Research Chair in Shared Decision Making and Knowledge Translation (6). This project aims to enable pregnant women and their partners to get the support they need to make informed decisions about prenatal screening and includes several phases. Participants and sample size: For phase 1, we propose to recruit an independent sample of 90 pregnant women and their partners from three clinical sites (a midwife-led birthing centre, a family practice clinic, and an obstetrician-led hospital clinic) in Quebec City and Montreal, Canada. Pregnant women and their partners will be recruited to reflect the respective proportions of socio-economic, ethnic, and linguistic communities. To be eligible for the study, pregnant women must be at least 18 years old, more than 20 weeks pregnant, have a low-risk pregnancy, not have given birth near the dates of data collection, be able to speak and write French or English and be able to give informed consent. Partners of pregnant women will also be asked to provide informed consent. Measured outcome: The primary outcome of this study is to measure the intention of pregnant women and/or their partners to use a mobile application for prenatal screening decision making. To assess this outcome, the Continuing Professional Development - Feedback (CPD-Feedback) questionnaire was used. This tool is a validated 12-item questionnaire that assesses the impact of continuing professional development activities on the clinical behavioural intentions of health professionals. We will also determine the factors (socio-demographic and others) potentially associated with intention. Statistical analysis: We will first perform descriptive statistics to determine the characteristics of our study population and the distribution of intention. Subsequently, a linear mixed model will be used to determine potential factors influencing the intention of pregnant women and/or their partners to use a mobile application for prenatal screening decision making. We will specify random effects at the practice level (cluster), which will allow us to answer our research question while considering the hierarchical structure of the study.

 

Références :
1. Ohman SG, Grunewald C, Waldenström U. Women's worries during pregnancy: testing the Cambridge Worry Scale on 200 Swedish women. Scand J Caring Sci. 2003 Jun;17(2):148-52. doi: 10.1046/j.1471-6712.2003.00095.x. PMID: 12753515 

 

2. Practice Bulletin No. 163: Screening for Fetal Aneuploidy. Obstet Gynecol. 2016 May;127(5): e123-e137. doi: 10.1097/AOG.0000000000001406. PMID: 26938574. 

 

3. Légaré F, St-Jacques S, Gagnon S, Njoya M, Brisson M, Frémont P, Rousseau F. Prenatal screening for Down syndrome: a survey of willing in women and family physicians to engage in shared decision-making. Prenat Diagn. 2011 Avr;31(4):319-26. doi: 10.1002/.2624. EPUB 2011 Jan 26. PMID : 21268046.

 

4. Agbadje TT, Pilon C, Bérubé P, Forest JC, Rousseau F, Rahimi SA, Giguère Y, Légaré F. User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study. JMIR Pediatr Parent. 6 septembre 2022;5(3):e35381. doi : 10.2196/35381. PMID : 35896164; Numéro PMCID : PMC9490528.

 

5. Delanoë A, Lépine J, Turcotte S, Leiva Portocarrero ME, Robitaille H, Giguère AM, Wilson BJ, Witteman HO, Lévesque I, Guillaumie L, Légaré F. Rôle des facteurs psychosociaux et de la littératie en santé dans l’intention des femmes enceintes d’utiliser un outil d’aide à la décision pour le dépistage du syndrome de Down : un sondage en ligne fondé sur la théorie. 2016 Oct 28;18(10):e283. doi : 10.2196/jmir.6362. PMID : 27793792; PMCID : PMC5106559.

 

6. Abbasgholizadeh Rahimi S, Lépine J, Croteau J, Robitaille H, Giguere AM, Wilson BJ, Rousseau F, Lévesque I, Légaré F. Facteurs psychosociaux de l’intention des professionnels de la santé d’utiliser un outil d’aide à la décision pour le dépistage du syndrome de Down : étude quantitative transversale. 2018 Apr 25;20(4):e114. doi : 10.2196/jmir.9036. PMID : 29695369; PMCID : PMC5943629.

Discover

Featured project

Prediction and early identification of stroke is crucial to prevent emergency department (ED) revisits and initiate treatment, reducing morbidity and mortality.

Read more