Affiliations: [a] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| [b] School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
Correspondence:
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Corresponding author. Yunfeng Peng, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China. E-mail: [email protected].
Abstract: Extracting and digitizing drug attributes from medical literature is the first step to build a knowledge computing system for precision disease treatment. In order to build a cardiovascular drug knowledge base, this paper proposes a multi-label text classification method for cardiovascular drug attributes from the Chinese drug guideline. The drug attributes are characterized by a BERT pre-trained model, and a dual-feature extraction structure is proposed based on the BiGRU neural network to capture high-level semantic information. Label categorization of cardiovascular drug attributes, such as indications and mode of administration, is accomplished. The F1 score of 0.8431 was obtained using 5-fold cross-validation. Comparing KNN and Naïve bayes, and conducting CNN and BiGRU control experiments on the basis of Word2Vec characterization of medication guidelines, the proposed multi-label text classification method is effective and the F1 value is significantly improved. Proved by analysis of ablation and crossover experiments, the proposed method can achieve a high accuracy rate averaged at 0.8339.
Keywords: Multi-label text classification, cardiovascular drug attributes, BERT, BiGRU, dual feature extraction
