Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have
been in use in a variety of intelligent medical applications. Thus, understanding the research …

Biomedical knowledge graphs (KGs), which can help with the understanding of complex
biological systems and pathologies, have begun to play a critical role in medical practice …

Abstract Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding
Models (KGEMs) have led to their adoption in a broad range of fields and applications. The …

Abstract Background Knowledge graphs (KGs) play a key role to enable explainable
artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs …

In machine learning often a tradeoff must be made between accuracy and intelligibility. More
accurate models such as boosted trees, random forests, and neural nets usually are not …

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses
knowledge graph reasoning models to predict new therapeutic pathways for existing drugs …

Objectives: Unexpected ICU readmission is associated with longer length of stay and
increased mortality. To prevent ICU readmission and death after ICU discharge, our team of …

Introduction Constructing an accurate and comprehensive knowledge graph of specific
diseases is critical for practical clinical disease diagnosis and treatment, reasoning and …

Decision making about discharge destination for critically ill patients is a highly subjective
and multidisciplinary process, heavily reliant on the ICU care team, patients and their …

Background Deep learning models have attracted significant interest from health care
researchers during the last few decades. There have been many studies that apply deep …