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BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning.

Fenghua Cai1, Jianfeng He1, Yunchuan Liu2

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.

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|May 24, 2024
PubMed
Summary

A new deep learning model, BCSLinker, improves the construction of Venous Thromboembolism knowledge graphs (VTEKG) from Chinese electronic medical records. This enhances VTE diagnosis, treatment, and patient self-care by reducing errors and redundancy.

Keywords:
Chinese electronic medical recordsdeep learningjoint entity and relation extractionknowledge graphvenous thromboembolism

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Knowledge Representation

Background:

  • Venous thromboembolism (VTE) presents significant morbidity and mortality, necessitating advanced knowledge management.
  • Existing methods for constructing VTE knowledge graphs (VTEKG) face challenges with error propagation and redundant information.
  • Effective VTEKG construction is crucial for integrating complex medical knowledge and analyzing entity relationships.

Purpose of the Study:

  • To develop a novel deep learning model for accurate and efficient VTEKG construction from Chinese electronic medical records.
  • To address limitations of current VTEKG construction methods, specifically error propagation and redundant data.
  • To create a structured data source for a VTE-specific question-answering system.

Main Methods:

  • Proposed the Biaffine Common-Sequence Self-Attention Linker (BCSLinker), a deep learning-based joint extraction model.
  • Utilized the Biaffine Common-Sequence Self-Attention (BCsSa) module for simultaneous entity and relation extraction, mitigating error propagation.
  • Employed multi-label cross-entropy loss to reduce the impact of redundant information.

Main Results:

  • Achieved a high F1 score of 86.9% on BCSLinker using VTE patient electronic medical record data.
  • Demonstrated superior performance compared to other joint entity and relation extraction models.
  • Successfully developed a question-answering system leveraging the constructed VTEKG.

Conclusions:

  • Successfully constructed a more accurate and comprehensive VTEKG.
  • The VTEKG provides valuable reference for VTE diagnosis, evaluation, and treatment.
  • The developed system supports clinical decision-making and patient self-care, offering considerable clinical value.