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A Syntactic Information-Based Classification Model for Medical Literature: Algorithm Development and Validation

Wentai Tang1, Jian Wang1, Hongfei Lin1

  • 1College of Computer Science and Technology, Dalian University of Technology, Dalian, China.

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|August 2, 2022
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Summary
This summary is machine-generated.

This study introduces a new syntactic information-based model to improve medical literature classification through relation extraction. The enhanced model significantly outperforms existing methods, boosting efficiency in organizing vast medical research.

Keywords:
classificationextractioninteractionliteraturemedical literaturemedical relation extractionmedical textneural networkspruning methodsemanticsyntacticsyntactic featurestext

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Area of Science:

  • Natural Language Processing
  • Bioinformatics
  • Computational Linguistics

Background:

  • The growing volume of medical literature requires effective classification methods.
  • Medical relation extraction is crucial for organizing research, evolving from rule-based to neural network models.
  • Current neural models often neglect valuable shallow syntactic information.

Purpose of the Study:

  • To develop a syntactic information-based relation extraction model for enhanced medical literature classification.
  • To improve the efficiency and accuracy of medical text analysis.

Main Methods:

  • Incorporated shallow syntactic information into a convolutional neural network to improve nonlocal syntactic interactions.
  • Developed a cross-domain pruning method to balance local and nonlocal syntactic interactions.

Main Results:

  • Achieved F1 scores of 65.5% (BioCreative ViCPR) and 91.5% (Phenotype-Gene Relationship).
  • Reached an accuracy of 88.7% on the PubMed dataset.
  • Outperformed current state-of-the-art baseline models on multiple datasets.

Conclusions:

  • The proposed syntactic information-based model effectively enhances medical relation extraction.
  • Shallow syntactic information aids in capturing nonlocal interactions and reinforcing syntactic features in sentences.
  • This approach offers novel directions for future research in medical text mining.