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Philippe Després
Professor
Faculté des sciences et de génie
Université Laval
Philippe Després is a full professor in the Department of Physics, Engineering Physics and Optics and member of the Cancer Research Center of Université Laval, medical physicist at CHU de Québec and a regular researcher at its affiliated Research Center, membrer of the Institute intelligence and data of Université Laval and member of the Researcher Council of the New Digital Research Infrastructure Organization (NDRIO). He is the designated principal investigator of the RHHDS program.
He was trained at Université Laval (MSc 2000, Physics), Université de Montréal (PhD 2005, Physics) and University of California, San Francisco (postdoc 2005-2007, Biomedical Engineering, Molecular Imaging).
Professor Philippe Després is involved in several projects encompassing hardware and software aspects of medical imaging modalities, notably low-dose X-ray imaging, advanced imaging techniques, and solid-state detectors for molecular imaging. He pioneered high-performance computing (HPC) approaches with commodity graphics hardware (GPUs) that led to innovative applications in image processing/reconstruction and radiation dose calculations, including a fast GPU-based Monte Carlo engine to simulate energy transport in matter (GPUMCD).
As a HPC expert, professor Philippe Després is also involved in data-driven research approaches, data infrastructures and FAIR-compliant research data management. In this regard, he is responsible of biomedical data at CHU de Québec of Université Laval Research Center, the data architect of the PULSAR health research platform, the assistant director of the Big Data Research Center at Université Laval, and the co-lead of the sustainable health axis of the Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique (OBVIA).
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Project and time management
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Reconciliation & EDI
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Communication skills
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Elsa Rousseau
Assistant Professor
Faculté des sciences et de génie
Université Laval
Elsa Rousseau est professeure adjointe au Département d’informatique et de génie logiciel de la Faculté des sciences et de génie à Université Laval, et au sein du Centre NUTRISS-Nutrition, santé et société. Son programme de recherche porte sur l’étude des interrelations entre le microbiote, les bactériophages et la nutrition, via l’élaboration d’approches en intelligence artificielle pour déterminer leurs impacts sur la santé cardiométabolique.
Elsa a obtenu son diplôme d’ingénieure en bio-informatique et modélisation à l'INSA Lyon en 2011, puis son doctorat de l’université de Nice Sophia Antipolis en 2016, en modélisation de l’épidémiologie et de l’évolution des virus. Elle a ensuite réalisé deux postdoctorats, un premier chez IBM, dans leur réputé Centre de recherche Almaden à San Jose (CA), en modélisation mathématique des dynamiques de populations virales pour l’élaboration d’un nouveau type de traitement, puis un second dans le laboratoire de Jacques Corbeil au Centre de recherche du CHU de Québec-Université Laval, en codirection avec François Laviolette, en bio-informatique et intelligence artificielle pour l’analyse de données métagénomique en santé.
Elsa est membre régulière du Centre de recherche en données massives de l'Université Laval (CRDM), de l’Institut intelligence et données (IID), de l’Institut sur la nutrition et les aliments fonctionnels (INAF), et membre associée à l’Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA).
Title of the research project
Detection of delirium using physiological parameters and hypovigilance monitoring: a pilot observational cohort study
Description
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.
Title of the research project
Development of GPU-based optimization algorithms for treatment planning in HDR brachytherapy
Description
High-dose-rate (HDR) brachytherapy is a standard treatment modality to treat cancer (e.g., prostate and cervical cancer) using the ionizing radiation of a small encapsulated radioactive source. The curative aim in the clinic is to create treatment plans that maximize the dose to the tumor while minimizing the dose to normal tissues. When it comes to the treatment plan generation, manual fine tuning of an objective function is necessary to achieve optimal trade-offs between these two conflicting objectives.
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
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.
Candide Ahouehome
M.Sc. candidate
Faculté de médecine
Université Laval
Student
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
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.
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Featured project
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.