3 results match your criteria: "Northern Jiangsu Province People Hospital of Yangzhou University[Affiliation]"

Article Synopsis
  • Constructing a knowledge graph for diseases, specifically heart failure, is important for enhancing clinical diagnosis, treatment, and health management, but current methods often struggle with limited training data and out-of-distribution entities.* -
  • This study introduces an innovative pipeline that uses large language models, prompt engineering, and expert refinement to improve the design and extraction phases of knowledge graph construction.* -
  • Results show the proposed TwoStepChat method significantly outperforms traditional methods, saving 65% of annotation time and effectively handling information not present in training data.*
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And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first construct a disease knowledge graph that focuses on the multi-view disease attributes of ICD codes and the disease relationships between these codes.

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