In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

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 …

Integrating modern machine learning and clinical decision-making has great promise for
mitigating healthcare's increasing cost and complexity. We introduce the Enhanced …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …

Abstract Background The proportion of Canadian youth seeking mental health support from
an emergency department (ED) has risen in recent years. As EDs typically address urgent …

Objective: To determine whether graph neural network based models of electronic health
records can predict specialty consultation care needs for endocrinology and hematology …

This article focuses on evaluating the efficacy of intelligent image processing techniques
using deep learning algorithms in the context of football, to present pragmatic solutions for …

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 …

Artificial intelligence (AI) models in medicine often fall short in real-world deployment due to
inability to incorporate multiple data modalities in their decision-making process as …

Hospital readmission prediction is defined as an evaluation task to model the historical
medical data to predict whether patients will be readmitted after discharge. In the past few …