Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture
the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG …

Background Scalable and accurate health outcome prediction using electronic health record
(EHR) data has gained much attention in research recently. Previous machine learning …

Objective Medical knowledge graph (KG) is attracting attention from both academic and
healthcare industry due to its power in intelligent healthcare applications. In this paper, we …

Graph representation learning is a method for introducing how to effectively construct and
learn patient embeddings using electronic medical records. Adapting the integration will …

Currently, there is a rapidly increasing need for high-quality biomedical knowledge graphs
(BioKG) that provide direct and precise biomedical knowledge. In the context of COVID-19 …

While recent developments of deep learning models have led to record-breaking
achievements in many areas, the lack of sufficient interpretation remains a problem for many …

Introduction With the advent of artificial intelligence, the secondary use of routinely collected
medical data from electronic healthcare records (EHR) has become increasingly popular …

To derive meaningful insights from voluminous healthcare data, it is essential to convert it
into machine understandable knowledge. Currently, machine understandable domain …

Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text
and medical codes. There is a wealth of expert-curated biomedical domain knowledge …

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