On the other hand, physicians also describe and write down important patient characteristics and disease-related symptoms for almost every medical encounter through free-form medical notes. In recent years, significant advances in multi-omics technology (e.g., genomics, transcriptomics, etc.) have also created unprecedented opportunities to characterize biological processes correlated with disease. However, combining these heterogeneous data sources in a meaningful way for disease prediction is challenging.
Therefore, for better precision medicine, physicians must now make increasingly complex therapeutic decisions with an unrealistic number of variables. Therefore, developments in artificial intelligence (AI) are envisioned to create a data science revolution in medicine. In particular, graphical neural networks (GNNs) have shown immense potential in learning meaningful and powerful data representations by combining the relational inference of graphical models with the power of deep learning. However, since the power of deep learning is strongly associated with data size and medical data cannot be easily shared among medical institutions due to patient privacy concerns, developing powerful GNN models for disease prediction in healthcare is a major challenge.
The figures below provide an overview of the main research framework of the MEDomics UdeS lab.
The most aggressive tumors tend to be more heterogeneous
Neural networks can detect the most heterogeneous tumors
Data from different sources can be combined to make better predictions
For example, medical imaging, physician text notes and clinical data are full of relevant information
To guarantee patient privacy while ensuring maximum use of relevant data
Decentralized learning to ensure medical center sovereignty
The MEDomics UdeS lab aims to promote the use of best practices in data science.
This laboratory is a sub-branch of the MEDomics consortium.
Visit the MEDomics website Main publication of the consortium
At Université de Sherbrooke, this laboratory is part of the Interdisciplinary Research Group in Health Informatics (GRIIS).