Objective: This study aims to comprehensively review the use of graph neural networks
(GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary …

While Multimodal Large Language Models (MLLMs) are widely used for a variety of vision-
language tasks, one observation is that they sometimes misinterpret visual inputs or fail to …

Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …

Images contain rich relational knowledge that can help machines understand the world.
Existing methods on visual knowledge extraction often rely on the pre-defined format (eg …

Abstract Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of
vision-language tasks. However, a challenge hindering their application in real-world …

Textual-edge Graphs (TEGs), characterized by rich text annotations on edges, are
increasingly significant in network science due to their ability to capture rich contextual …

Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical
research and practice. In recent years, deep learning models have been applied to EHRs …