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.

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.