{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:58:36Z","timestamp":1777705116076,"version":"3.51.4"},"reference-count":11,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>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 Na\u00efve 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.<\/jats:p>","DOI":"10.3233\/jifs-236115","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T10:15:41Z","timestamp":1709892941000},"page":"10683-10693","source":"Crossref","is-referenced-by-count":4,"title":["Multi-label text classification of cardiovascular drug attributes based on BERT and BiGRU"],"prefix":"10.1177","volume":"46","author":[{"given":"Hongzhen","family":"Cui","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"}]},{"given":"Longhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"}]},{"given":"Xiaoyue","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"}]},{"given":"Xiuping","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Yunfeng","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-236115_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IRASET52964.2022.9738147"},{"issue":"4","key":"10.3233\/JIFS-236115_ref2","doi-asserted-by":"publisher","first-page":"392","DOI":"10.3390\/healthcare8040392","article-title":"Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method[J]","volume":"8","author":"Ahmed","year":"2020","journal-title":"Healthcare (Basel)"},{"issue":"9","key":"10.3233\/JIFS-236115_ref3","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification[J]","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognition"},{"issue":"3","key":"10.3233\/JIFS-236115_ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jdwm.2007070101","article-title":"Multi-label classification: An overview[J]","volume":"3","author":"Tsoumakas","year":"2007","journal-title":"Int\u2019l Journal of Data Warehousing and Mining (IJDWM)"},{"issue":"8","key":"10.3233\/JIFS-236115_ref5","first-page":"16","article-title":"A multi-label classification algorithm based on label clustering[J]","volume":"35","author":"Shen","year":"2014","journal-title":"Software"},{"issue":"3","key":"10.3233\/JIFS-236115_ref7","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1177\/1550147720911892","article-title":"A novel multi-label classification algorithm based on K-nearest neighbor and random walk[J]","volume":"16","author":"Wang","journal-title":"International Journal of Distributed Sensor Networks"},{"issue":"8","key":"10.3233\/JIFS-236115_ref8","doi-asserted-by":"crossref","first-page":"6354","DOI":"10.1016\/j.jksuci.2021.02.014","article-title":"Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm[J]","volume":"34","author":"Purvi","year":"2022","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"issue":"09","key":"10.3233\/JIFS-236115_ref13","first-page":"1182","article-title":"Localization model of traditional Chinese medicine Zang-fu based on ALBERT and BiGRU[J]","volume":"43","author":"Zhang","year":"2021","journal-title":"Chinese Journal of Engineering"},{"issue":"4","key":"10.3233\/JIFS-236115_ref14","first-page":"1079","article-title":"Multi-label text classification method based on label semantic information[J]","volume":"31","author":"Xiao","year":"2020","journal-title":"Journal of Software"},{"key":"10.3233\/JIFS-236115_ref17","first-page":"2227","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding[C]","author":"Devlin","year":"2019","journal-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"10.3233\/JIFS-236115_ref18","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.ress.2019.01.006","article-title":"Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process[J]","volume":"185","author":"Chen","year":"2019","journal-title":"Reliability Engineering and System Safety"}],"container-title":["Journal of Intelligent &amp; 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