Abstract Medical Knowledge Graph (KG) has shown great potential in various healthcare
scenarios, such as drug recommendation and clinical decision support system. The factors …

Automatic medical entity and relation extraction is essential for daily electronic medical
record (EMR) analysis, and has attracted a lot of academic attention. Tremendous progress …

Background Patients with type 2 diabetes (T2DM) have an increasing need for personalized
and Precise management as medical technology advances. Artificial intelligence (AI) …

Abstract Uncertain Knowledge Graphs (UKGs) are used to characterize the inherent
uncertainty of knowledge and have a richer semantic structure than deterministic knowledge …

Pig disease knowledge graphs (KGs) are crucial for the prevention and treatment of pig
diseases. Due to the difficulty of knowledge mining in the field of traditional animal …

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity
disambiguation (NED). EL models are trained on corpora labeled by a predefined KB …

Lesion prediction, a very important aspect of cancer disease prediction, is an important
marker for patients before they become cancerous. Currently, traditional machine learning …

Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich
information for downstream applications. Although extracting triples from unstructured texts …

Modern medicine emphasizes interpretability and requires doctors to give reasonable, well-
founded and convincing diagnostic results when diagnosing patients. Therefore, there are a …

Abstract Named Entity Recognition (NER) in Chinese Electronic Medical Records (EMRs) is
crucial for enhancing healthcare quality and efficiency. However, the unique complexity of …