Medical knowledge graph can potentially help knowledge discovery from clinical data,
assisting clinical decision making and personalized treatment recommendation. This paper …

The incorporation of data analytics in the healthcare industry has made significant progress,
driven by the demand for efficient and effective big data analytics solutions. Knowledge …

Given the risks and cost of hospitalization, there has been significant interest in exploiting
machine learning models to improve perioperative care. However, due to the high …

Objective Identifying sets of rare diseases with shared aspects of etiology and
pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative …

Identifying new potential treatment options (say, medications and procedures) for known
medical conditions that cause human disease burden is a central task of biomedical …

Representation learning of medical Knowledge Graph (KG) is an important task and forms
the fundamental process for intelligent medical applications such as disease diagnosis and …

Graph data models are an emerging approach to structure clinical and biomedical
information. These models offer intriguing opportunities for novel approaches in healthcare …

Knowledge representation learning represents entities and relations of knowledge graph in
a continuous low-dimensional semantic space. Recently, various representation learning …

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically
learn medical knowledge. In one prominent example, a causal health knowledge graph …

Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug
discovery and pharmaceutical research as they provide a structured way to integrate diverse …