Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …

Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of
patient information that can inform clinical decision-making. However, the sheer volume and …

Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's
health status, supporting various predictive healthcare tasks. Recently, several studies have …

Alzheimer's disease (AD) is a degenerative disorder that encompasses multiple stages
during its onset. There are certain shared characteristics among patients at various stages of …

Graph deep learning (GDL) has demonstrated impressive performance in predicting
population-based brain disorders (BDs) through the integration of both imaging and non …

Deep learning enables automatic and robust extraction of cardiac function descriptors from
echocardiographic sequences, such as ejection fraction or strain. These descriptors provide …

The widespread adoption of Electronic Health Records (EHRs) and deep learning,
particularly through Self-Supervised Representation Learning (SSRL) for categorical data …

Population graphs and their use in combination with graph neural networks (GNNs) have
demonstrated promising results for multi-modal medical data integration and improving …

L'hypertension artérielle (HT) est la maladie cardiovasculaire la plus répandue dans le
monde, affectant plus de 30% des adultes dans le monde en 2019, soit 1, 28 milliard de …