Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …

The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …

Electronic health records (EHRs) are an increasingly important source of information for
healthcare professionals and researchers. However, EHRs are often fragmented …

Background The medical knowledge graph provides explainable decision support, helping
clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical …

Nursing and informatics share a common strength in their use of structured representations
of domains, specifically the underlying notion of 'things'(ie, concepts, constructs, or named …

Automated knowledge curation for biomedical ontologies is key to ensure that they remain
comprehensive, high-quality and up-to-date. In the era of foundational language models …

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

Knowledge Graph Embedding (KGE) techniques are crucial in learning compact
representations of entities and relations within a knowledge graph, facilitating efficient …

Objectives This study aimed to explore the value of the Charlson comorbidity index (CCI) in
predicting ICU admission in patients with aortic aneurysm (AA). Methods The clinical data of …

Accurate prediction of unplanned readmissions allows healthcare systems to adopt
preventive measures, reducing these occurrences. Creating a model that accurately predicts …