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

The development of electronic health records (EHR) systems has enabled the collection of a
vast amount of digitized patient data. However, utilizing EHR data for predictive modeling …

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction
for the events of interest can support an informative outlook for a patient's medical journey …

Recent advancements in sequential modeling applied to Electronic Health Records (EHR)
have greatly influenced prescription recommender systems. While the recent literature on …

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 Medication recommendation using Electronic Health Records (EHR) is challenging
due to complex medical data. Current approaches extract longitudinal information from …

Ontologies are widely used for representing domain knowledge and meta data, playing an
increasingly important role in Information Systems, the Semantic Web, Bioinformatics and …

Recommending medications with electronic health records (EHRs) is a challenging task for
data-driven clinical decision support systems. Most existing models learnt representations …

Over the past decade, significant advancements in mining electronic health records (EHRs)
have enabled a broad range of decision-support applications and offered an unprecedented …

The use of electronic health records has become widespread, providing a valuable source
of information for predicting disease risk. While deep neural network models have been …