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.
Faculté des sciences et de génie
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).
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.
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.
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.
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.
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.
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 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.
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.