Ongoing projects

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.

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.

Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death among men. To improve patient outcomes, treatment must be personalized based on accurate prognosis. Nomograms already exist to identify patients at low risk for recurrence based on preoperative clinical information, but these tools do not use patients’ medical images.

The goal of this project is to use deep learning to develop a model combining FDG-PET/CT images and patient clinical data to improve the pre-treatment prognosis of high-grade prostate cancer. This model must be efficient, but also interpretable in order to allow an expert to understand the given probabilities.

Patient portal is an emerging healthcare technology that has shown promising effects in enhancing patient care experience and promoting patient health outcomes. Opal, a digital patient-centred portal, is currently available to patients at the Cedars Cancer Center at McGill University Health Centre (MUHC), providing real-time access to personal health information (upcoming appointments, clinical notes, lab results, etc.) in conjunction with the disease- and treatment-specific education materials. This project aims to quantify the initial impact of the Opal patient portal on operational and health-economic outcomes at the Cedars Cancer Centre. We leverage patient-level data from the Opal patient portal and MUHC. The primary method is to use propensity score matching to construct a matched cohort that compares operational outcomes and resource utilization between Opal users to non-users.

The planned outcomes of interest include missed appointments, emergency room visits, hospitalization, medical record requests, and fertility clinic appointments

Cancer patients go through a lot during treatment. Many patients need support from other patients who know what they are going through because family and friends, no matter how supportive they try to be, don’t always understand their struggles.
The Opal Health Informatics Group is developing two programs for supporting cancer patients in the portal Opal: OncoBuddy and OncoConseil. OncoBuddy is a support system that matches cancer patients with volunteer patients (we call them buddies) based on a matching algorithm that considers criteria selected by the patients themselves. OncoConseil, on the other hand, matches patients with threads of information that might interest them, such as tips and tricks to get a smoother experience during the treatment.
This project focuses on finding selection criteria for building the matching algorithm. Using semi-structured interviews with cancer patients, we will extract a panel of selection criteria that are the most valued by patients, such as type of cancer, age and gender of the buddy, stage of cancer, etc. 
 

Cancer patient peer support is a beneficial tool for current patients and previous patients who can share lived experiences. However, current peer support is inefficient as it is conducted manually and relies on a coordinator to match patients based on a few known factors.

Therefore, this research study will examine ways to develop AI-powered matching algorithms that will more efficiently and effectively match cancer patients according to a wider and more complex set of factors than can be done manually. In this research project, we are designing and developing an AI-matching algorithm for the OncoBuddy/OncoConseil project and evaluating its effectiveness to ensure recommended matches will result in appropriate peer support. We are comparing multiple existing AI models, (i.e. the Deferred Acceptance Algorithm and the Genetic Algorithm) and testing the models on synthetic patient data that we have generated with statistical inferences from the existing Opal database and Statistics Canada. A fitness function derived from previous research will determine the efficacy of the matching algorithms.

As a result, we have generated a synthetic dataset of 1770 patients to be used for training and testing purposes, implemented multiple AI algorithms, and deployed a live dashboard prototype.

The cancer experience and the uncertainty surrounding it is anxiety provoking. One way in which the non-clinical uncertainty of the cancer experience can be reduced is through peer support. The Opal Health Informatics Group seeks to evaluate the efficacy of an artificial intelligence-based peer support matching algorithm in the pre-existing patient portal Opal in the hopes of facilitating peer support programs (for cancer patients and their caregivers) in Quebec.

Our work focused on the architectural design, development, and demonstration of a proof of concept stand-alone web application used to demonstrate the results of several varieties of AI-powered matching algorithms on test patient data.

Delirium is a condition that, when left unmanaged, is associated with increased mortality and longer hospitalization of patients in intensive care; therefore, its detection should be an integral part of care. It is characterized by confusion, anxiety and reduced alertness. It is estimated that 75% of delirium cases are not detected on admission to hospital. Detecting such an acute condition requires frequent monitoring of participants, which is labor intensive and requires expertise. However, the participants' vital signs, which can be collected continuously throughout their stay in intensive care, could contain information indicative of the present state of consciousness, and possibly predictive of the future state.
Our goal is to build an automatic machine learning classifier based on vital sign data to (a) identify times when the patient was delirious, and (b) predict delirium incipience. As a primary measure, we will use a clinically validated tool, the Confusion Assessment Method for Intensive Care Unit (CAM-ICU). This assessment was performed twice a day, once in the morning and once in the afternoon, in our study population at the CISSS de Chaudière-Appalache (Hotel Dieu de Lévis). The learning algorithm will be trained on the participants' vital signs before, after, and during the delirium episodes in order to (a) extract the vital sign characteristics related to a delirium state; (b) the probability that the patient is delirious or not, based on these characteristics; and (c) the probability that the patient will develop a delirium state within a reasonable time window (e.g. 1 hour).
Even if the machine learning model does not reach the accuracy and precision of a validated questionnaire, its use in healthcare facilities would optimize care, mainly by drawing attention to any suspicious drift (high sensitivity). Considering that patients who remain with untreated delirium are associated with higher mortality rates and longer ICU stays, a clinical indicator such as this model can help the care team manage this otherwise unnoticed symptom.
 

The risk associated with the stochastic effects of neutron radiation is known to be strongly energy dependent. Over the past decade, several studies have used Monte Carlo simulations to estimate the relative biological effectiveness (RBE) of neutrons for various types of DNA damage in order to understand its energy dependence at the fundamental level. However, none of these studies implemented DNA repair simulations in their pipeline.

In this project, we investigated the effects of adding repair mechanisms to Monte Carlo-based RBE estimates of DNA damage by neutrons. Our group had previously carried out condensed history (CH) simulations to profile the energy spectrum and relative dose contribution of the secondary particles produced by neutron interactions in tissue. In this project, we use the results of our CH simulations to simulate the irradiation of TOPAS-nBio’s DNA model by a flat spectrum of neutrons ranging from 1 eV to 10 MeV, as well as reference X-rays at 250 keV. Induced DNA damage are recorded using the standard DNA damage data (SDD) format abd DNA repair are simulated using the DNA Mechanistic Repair Simulator (DaMaRiS) framework.

The project consists in determining and exploring the possibilities offered by dynamic dashboards in a medical context as well as the associated data management structures. The project therefore considers several aspects of data management. In this sense, the considerations related to DICOM data transfers as well as different approaches to their management and conservation are considered. In addition, the dashboards will be designed to ensure an effective, clear and concise presentation with recognized visualization tools. Different additions will be made to the different portions of the project during its implementation depending on the direction taken by the research and the needs of health professionals. Particular emphasis is placed on compliance with FAIR principles by the resulting system.