Research on Semantic Feature Enhancement for Medical Literature Classification

Corresponding author: anxinying, an.xinying@imicams.ac.cn

DOI: 10.12201/bmr.202411.00088

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  Abstract: Purpose/Significance The rapid growth of medical literature poses new challenges for literature classification,it is very important to build an effective automatic classification model of medical literature.Method/Process Using medical literature as data source，this article utilizes the synonyms and hierarchical structure of the MeSH vocabulary to enhance the features of concept information,uses the BERT model for fine-tuning and testing,and compares the classification results with random forest algorithm.Result/Conclusion The results of the ten-fold cross-validation method show that the precision,recall and F1 score of the medical literature classification model based on Mesh and BERT are 95.42%，93.61%，94.47%, which are better than the classification results of random forest and pure BERT.The medical literature classification model based on Mesh and BERT shows high accuracy and effectiveness, and has certain applicability.

  Key words: medical literature; MeSH; BERT; automatic classification

  Submit time: 29 November 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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