{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:24:39Z","timestamp":1772778279392,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T00:00:00Z","timestamp":1575936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFE0117500"],"award-info":[{"award-number":["2017YFE0117500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Medical text categorization is a specific area of text categorization. Classification for medical texts is considered a special case of text classification. Medical text includes medical records and medical literature, both of which are important clinical information resources. However, medical text contains complex medical vocabularies, medical measures, which has problems with high-dimensionality and data sparsity, so text classification in the medical domain is more challenging than those in other general domains. In order to solve these problems, this paper proposes a unified neural network method. In the sentence representation, the convolutional layer extracts features from the sentence and a bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences, which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. Experimental verifications are conducted on four medical text datasets, including two medical record datasets and two medical literature datasets. The results clearly show that our method is effective.<\/jats:p>","DOI":"10.3390\/fi11120255","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T10:52:41Z","timestamp":1575975161000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Novel Neural Network-Based Method for Medical Text Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7002-7127","authenticated-orcid":false,"given":"Li","family":"Qing","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7182-331X","authenticated-orcid":false,"given":"Weng","family":"Linhong","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-007X","authenticated-orcid":false,"given":"Ding","family":"Xuehai","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","article-title":"Machine learning in automated text categorization","volume":"34","author":"Sebastiani","year":"2002","journal-title":"ACM Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.eswa.2018.09.034","article-title":"Clinical text classification research trends: Systematic literature review and open issues","volume":"116","author":"Mujtaba","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_3","first-page":"255","article-title":"Classification of free text clinical narratives (short review)","volume":"124","author":"Kaurova","year":"2011","journal-title":"Bus. Eng. Appll. Intell. Inf. Syst."},{"key":"ref_4","unstructured":"Hoang, N., and Patrick, J. (2016, January 13\u201317). Text Mining in Clinical Domain: Dealing with Noise. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA."},{"key":"ref_5","first-page":"38","article-title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","volume":"394\u2013395","author":"Huang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_6","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., and Bengio, Y. (2014, January 12). Empirical evaluation of gated recurrent neural networks on sequence modeling. Proceedings of the NIPS 2014 Learning and Representation Learning Workshop, Montreal, QC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wallach, H.M. (2006, January 25\u201329). Topic modeling: Beyond bag-of-words. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143967"},{"key":"ref_9","unstructured":"Yoon, K. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the 19th Conference on Empirical Methods in Natural Language Processing, Doha, Qatar."},{"key":"ref_10","unstructured":"Johnson, R., and Tong, Z. (5, January 31). Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. Proceedings of the HLT-NAACL, San Francisco, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, S., Zhao, Z., Liu, T., Hu, R., and Du, X. (2017, January 9\u201311). Initializing Convolutional Filters with Semantic Features for Text Classification. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1201"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Johnson, R., and Tong, Z. (2017, January 9\u201311). Deep Pyramid Convolutional Neural Networks for Text Categorization. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/P17-1052"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, B. (2018, January 15\u201320). Disconnected Recurrent Neural Networks for Text Categorization. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (LongPapers), Melbourne, VI, Australia.","DOI":"10.18653\/v1\/P18-1215"},{"key":"ref_14","unstructured":"Liu, P., Qiu, X., and Huang, X. (2016, January 9\u201315). Recurrent Neural Network for Text Classification with Multi-Task Learning. Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., and Zhao, J. (2015, January 25\u201330). Recurrent Convolutional Neural Networks for Text Classification. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"ref_16","unstructured":"Zhou, C., Sun, C., Liu, Z., and Lau, F. (2015, November 30). A C-LSTM Neural Network for Text Classification. Available online: https:\/\/arxiv.gg363.site\/abs\/1511.08630."},{"key":"ref_17","unstructured":"Yao, L., Zhang, Y., Wei, B., Li, Z., and Huang, X. (2016, January 15\u201318). Traditional Chinese medicine clinical records classification using knowledge-powered document embedding. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicinee, Shenzhen, China."},{"key":"ref_18","first-page":"246","article-title":"Medical text classification using convolutional neural networks","volume":"235","author":"Hughes","year":"2017","journal-title":"Stud. Health Technol."},{"key":"ref_19","unstructured":"Baker, S., Korhonen, A., and Pyysalo, S. (2016, January 12). Cancer hallmark text classification using convolutional neural networks. Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016), Osaka, Japan."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 17). Hierarchical Attention Networks for Document Classification. Proceedings of the NAACL-HLT, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_21","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013, September 07). Efficient Estimation of Word Representations in Vector Space. Available online: https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"ref_22","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012, July 03). Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. Available online: https:\/\/arxiv.org\/abs\/1207.0580."},{"key":"ref_23","unstructured":"Graves, A. (2014, June 05). Generating Sequences with Recurrent Neural Networks. Available online: https:\/\/arxiv.org\/abs\/1308.0850."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/bioinformatics\/btv585","article-title":"Automatic semantic classification of scientific literature according to the hallmarks of cancer","volume":"32","author":"Baker","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_25","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","first-page":"394","article-title":"A method for multi-class sentiment classification based on an improved one-vs-one (ovo) strategy and the support vector machine (svm) algorithm","volume":"8","author":"Liu","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_27","unstructured":"Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016, August 09). Bag of Tricks for Efficient Text Classification. Available online: https:\/\/arxiv.org\/abs\/1607.01759."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1198\/004017007000000245","article-title":"Large-Scale Bayesian Logistic Regression for Text Categorization","volume":"49","author":"Genkin","year":"2004","journal-title":"Technometrics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","article-title":"Bidirectional LSTM with attention mechanism and convolutional layer for text classification","volume":"337","author":"Gang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J.A. (2014, January 14\u201316). A method for stochastic optimization. Proceedings of the 3rd International Conference for Learning Representations, San Diego, CA, USA."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/12\/255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:54Z","timestamp":1760190054000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/12\/255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,10]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["fi11120255"],"URL":"https:\/\/doi.org\/10.3390\/fi11120255","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,10]]}}}