{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:38:11Z","timestamp":1773214691341,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031162091","type":"print"},{"value":"9783031162107","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16210-7_8","type":"book-chapter","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T23:03:09Z","timestamp":1663714989000},"page":"101-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving Bert-Based Model for\u00a0Medical Text Classification with\u00a0an\u00a0Optimization Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0138-2226","authenticated-orcid":false,"given":"Karim","family":"Gasmi","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"8_CR1","unstructured":"Baker, S., Korhonen, A., Pyysalo, S.: Cancer hallmark text classification using convolutional neural networks. In: Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016), pp. 1\u20139 (2016)"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Boudjellal, N., et al.: ABioNER: a BERT-based model for Arabic biomedical named-entity recognition. Complexity 2021 (2021)","DOI":"10.1155\/2021\/6633213"},{"key":"8_CR3","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"8_CR4","unstructured":"Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69\u201378 (2014)"},{"issue":"4","key":"8_CR5","first-page":"499","volume":"93","author":"BG Druss","year":"2005","unstructured":"Druss, B.G., Marcus, S.C.: Growth and decentralization of the medical literature: implications for evidence-based medicine. J. Med. Libr. Assoc. 93(4), 499 (2005)","journal-title":"J. Med. Libr. Assoc."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Gasmi, K.: Hybrid deep learning model for answering visual medical questions. Supercomputing (2022)","DOI":"10.1007\/s11227-022-04474-8"},{"key":"8_CR7","first-page":"1","volume":"53","author":"K Gasmi","year":"2021","unstructured":"Gasmi, K., Ltaifa, I.B., Lejeune, G., Alshammari, H., Ammar, L.B., Mahmood, M.A.: Optimal deep neural network-based model for answering visual medical question. Cybernet. Syst. 53, 1\u201322 (2021)","journal-title":"Cybernet. Syst."},{"issue":"6","key":"8_CR8","doi-asserted-by":"publisher","first-page":"814","DOI":"10.5370\/JEET.2012.7.6.814","volume":"7","author":"JH Heo","year":"2012","unstructured":"Heo, J.H., Lyu, J.K., Kim, M.K., Park, J.K.: Application of particle swarm optimization to the reliability centered maintenance method for transmission systems. J. Electr. Eng. Technol. 7(6), 814\u2013823 (2012)","journal-title":"J. Electr. Eng. Technol."},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Hrizi, O., et al.: Tuberculosis disease diagnosis based on an optimized machine learning model. J. Healthc. Eng. 2022 (2022)","DOI":"10.1155\/2022\/8950243"},{"key":"8_CR10","unstructured":"Huang, K., Altosaar, J., Ranganath, R.: ClinicalBERT: modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342 (2019)"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Jabbar, R., Fetais, N., Krichen, M., Barkaoui, K.: Blockchain technology for healthcare: enhancing shared electronic health record interoperability and integrity. In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 310\u2013317. IEEE (2020)","DOI":"10.1109\/ICIoT48696.2020.9089570"},{"issue":"3","key":"8_CR12","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1087\/20100308","volume":"23","author":"AE Jinha","year":"2010","unstructured":"Jinha, A.E.: Article 50 million: an estimate of the number of scholarly articles in existence. Learn. Publ. 23(3), 258\u2013263 (2010)","journal-title":"Learn. Publ."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)","DOI":"10.3115\/v1\/P14-1062"},{"issue":"3","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.32604\/cmc.2021.014090","volume":"66","author":"M Krichen","year":"2021","unstructured":"Krichen, M., et al.: A formal testing model for operating room control system using internet of things. Comput. Mater. Continua 66(3), 2997\u20133011 (2021)","journal-title":"Comput. Mater. Continua"},{"issue":"3","key":"8_CR15","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s11192-010-0202-z","volume":"84","author":"P Larsen","year":"2010","unstructured":"Larsen, P., Von Ins, M.: The rate of growth in scientific publication and the decline in coverage provided by science citation index. Scientometrics 84(3), 575\u2013603 (2010)","journal-title":"Scientometrics"},{"issue":"4","key":"8_CR16","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Lin, J., Karakos, D., Demner-Fushman, D., Khudanpur, S.: Generative content models for structural analysis of medical abstracts. In: Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis, pp. 65\u201372 (2006)","DOI":"10.3115\/1567619.1567631"},{"issue":"21","key":"8_CR18","doi-asserted-by":"publisher","first-page":"10739","DOI":"10.1007\/s00500-018-3628-5","volume":"23","author":"CJ Mantas","year":"2018","unstructured":"Mantas, C.J., Castellano, J.G., Moral-Garc\u00eda, S., Abell\u00e1n, J.: A comparison of random forest based algorithms: random credal random forest versus oblique random forest. Soft. Comput. 23(21), 10739\u201310754 (2018). https:\/\/doi.org\/10.1007\/s00500-018-3628-5","journal-title":"Soft. Comput."},{"key":"8_CR19","unstructured":"McKnight, L., Srinivasan, P.: Categorization of sentence types in medical abstracts. In: AMIA Annual Symposium Proceedings, vol. 2003, p. 440. American Medical Informatics Association (2003)"},{"issue":"12","key":"8_CR20","first-page":"111","volume":"3","author":"K Mittal","year":"2017","unstructured":"Mittal, K., Khanduja, D., Tewari, P.C.: An insight into \u2018decision tree analysis\u2019. World Wide J. Multidisc. Res. Dev. 3(12), 111\u2013115 (2017)","journal-title":"World Wide J. Multidisc. Res. Dev."},{"issue":"8","key":"8_CR21","doi-asserted-by":"publisher","first-page":"4022","DOI":"10.3390\/ijerph18084022","volume":"18","author":"H Mukhtar","year":"2021","unstructured":"Mukhtar, H., Rubaiee, S., Krichen, M., Alroobaea, R.: An IoT framework for screening of COVID-19 using real-time data from wearable sensors. Int. J. Environ. Res. Public Health 18(8), 4022 (2021)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Parlak, B., Uysal, A.K.: Classification of medical documents according to diseases. In: 2015 23nd Signal Processing and Communications Applications Conference (SIU), pp. 1635\u20131638. IEEE (2015)","DOI":"10.1109\/SIU.2015.7130164"},{"issue":"8","key":"8_CR23","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977\u2013984 (2006)","DOI":"10.1145\/1143844.1143967"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Wang, B.: Disconnected recurrent neural networks for text categorization. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2311\u20132320 (2018)","DOI":"10.18653\/v1\/P18-1215"},{"key":"8_CR26","unstructured":"Xiao, Y., Cho, K.: Efficient character-level document classification by combining convolution and recurrent layers. arXiv preprint arXiv:1602.00367 (2016)"},{"key":"8_CR27","unstructured":"Yao, L., Zhang, Y., Wei, B., Li, Z., Huang, X.: Traditional Chinese medicine clinical records classification using knowledge-powered document embedding. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1926\u20131928. IEEE (2016)"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16210-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T12:16:40Z","timestamp":1678364200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16210-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031162091","9783031162107"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16210-7_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}