{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T08:19:24Z","timestamp":1772525964072,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T00:00:00Z","timestamp":1598572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T00:00:00Z","timestamp":1598572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Backgrounds<\/jats:title><jats:p>Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by different doctors under their hospital guidelines, making the final data rich in author- and domain-specific idiosyncrasies. Therefore, breast cancer treatment entity normalization becomes an essential task for the above downstream clinical studies. The latest studies have demonstrated the superiority of deep learning methods in named entity normalization tasks. Fundamentally, most existing approaches adopt pipeline implementations that treat it as an independent process after named entity recognition, which can propagate errors to later tasks. In addition, despite its importance in clinical and translational research, few studies directly deal with the normalization task in Chinese clinical text due to the complexity of composition forms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To address these issues, we propose PASCAL, an end-to-end and accurate framework for breast cancer treatment entity normalization (TEN). PASCAL leverages a gated convolutional neural network to obtain a representation vector that can capture contextual features and long-term dependencies. Additionally, it treats treatment entity recognition (TER) as an auxiliary task that can provide meaningful information to the primary TEN task and as a particular regularization to further optimize the shared parameters. Finally, by concatenating the context-aware vector and probabilistic distribution vector from TEN, we utilize the conditional random field layer (CRF) to model the normalization sequence and predict the TEN sequential results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To evaluate the effectiveness of the proposed framework, we employ the three latest sequential models as baselines and build the model in single- and multitask on a real-world database. Experimental results show that our method achieves better accuracy and efficiency than state-of-the-art approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The effectiveness and efficiency of the presented pseudo cascade learning framework were validated for breast cancer treatment normalization in clinical text. We believe the predominant performance lies in its ability to extract valuable information from unstructured text data, which will significantly contribute to downstream tasks, such as treatment recommendations, breast cancer staging and careflow mining.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-020-01216-9","type":"journal-article","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T12:03:05Z","timestamp":1598616185000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PASCAL: a pseudo cascade learning framework for breast cancer treatment entity normalization in Chinese clinical text"],"prefix":"10.1186","volume":"20","author":[{"given":"Yang","family":"An","sequence":"first","affiliation":[]},{"given":"Jianlin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-1527","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hanyu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhan","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhenguang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zengtao","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xiaopeng","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,28]]},"reference":[{"issue":"1687-8450","key":"1216_CR1","doi-asserted-by":"publisher","first-page":"509036","DOI":"10.1155\/2011\/509036","volume":"2011","author":"L Marklund","year":"2011","unstructured":"Marklund L, Hammarstedt L. Impact of hpv in oropharyngeal cancer. J Oncol. 2011; 2011(1687-8450):509036. https:\/\/doi.org\/10.1155\/2011\/509036.","journal-title":"J Oncol"},{"key":"1216_CR2","unstructured":"What Is Breast Cancer?https:\/\/www.imaginis.com\/general-information-on-breast-cancer\/what-is-breast-cancer-2. Accessed 11 June 2008."},{"key":"1216_CR3","doi-asserted-by":"publisher","unstructured":"Dagliati A, Sacchi L, Zambelli A, Tibollo V, Pavesi L, Holmes JH, Bellazzi R. Temporal electronic phenotyping by mining careflows of breast cancer patients. J Biomed Inform; 66:136\u201347. https:\/\/doi.org\/10.1016\/j.jbi.2016.12.012.","DOI":"10.1016\/j.jbi.2016.12.012"},{"issue":"10","key":"1216_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.5120\/13285-0747","volume":"76","author":"R Yadav","year":"2014","unstructured":"Yadav R, Khan Z, Saxena H. Chemotherapy prediction of cancer patient by using data mining techniques. Int J Comput Appl. 2014; 76(10):28\u201331. https:\/\/doi.org\/10.5120\/13285-0747.","journal-title":"Int J Comput Appl"},{"key":"1216_CR5","doi-asserted-by":"publisher","unstructured":"Wang XH, Zheng B, Good WF, King JL, Chang Y-H. Computer-assisted diagnosis of breast cancer using a data-driven bayesian belief network. Int J Med Inform; 54(2):115\u201326. https:\/\/doi.org\/10.1016\/S1386-5056(98)00174-9.","DOI":"10.1016\/S1386-5056(98)00174-9"},{"key":"1216_CR6","doi-asserted-by":"publisher","unstructured":"Kate RJ, Nadig R. Stage-specific predictive models for breast cancer survivability. Int J Med Inform; 97:304\u201311. https:\/\/doi.org\/10.1016\/j.ijmedinf.2016.11.001.","DOI":"10.1016\/j.ijmedinf.2016.11.001"},{"key":"1216_CR7","first-page":"1","volume":"abs\/1610.02527","author":"J Konecn\u00fd","year":"2016","unstructured":"Konecn\u00fd J, McMahan HB, Ramage D, Richt\u00e1rik P. Federated optimization: Distributed machine learning for on-device intelligence. ArXiv. 2016; abs\/1610.02527:1\u201338.","journal-title":"ArXiv"},{"issue":"2","key":"1216_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol. 2019; 10(2):12\u201311219. https:\/\/doi.org\/10.1145\/3298981.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"1216_CR9","doi-asserted-by":"publisher","first-page":"259","DOI":"10.21147\/j.issn.1000-9604.2019.02.02","volume":"31","author":"N PRC","year":"2019","unstructured":"PRC N. Chinese guidelines for diagnosis and treatment of breast cancer 2018 (english version). Chin J Cancer Res. 2019; 31:259\u201377. https:\/\/doi.org\/10.21147\/j.issn.1000-9604.2019.02.02.","journal-title":"Chin J Cancer Res"},{"key":"1216_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1186\/s12859-019-3071-3","volume":"20","author":"X Li","year":"2019","unstructured":"Li X, Fu C, Zhong R, Zhong D, He T, Jiang X. A hybrid deep learning framework for bacterial named entity recognition with domain features. BMC Bioinformatics. 2019; 20:583. https:\/\/doi.org\/10.1186\/s12859-019-3071-3.","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"1216_CR11","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TCBB.2018.2868346","volume":"17","author":"L Li","year":"2020","unstructured":"Li L, Jiang Y. Integrating language model and reading control gate in blstm-crf for biomedical named entity recognition. IEEE\/ACM Trans Comput Biol Bioinforma. 2020; 17(3):841\u2013846.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"issue":"Suppl 10","key":"1216_CR12","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1186\/s12859-019-2813-6","volume":"20","author":"W Yoon","year":"2019","unstructured":"Yoon W, So CH, Lee J, Kang J. Collabonet: collaboration of deep neural networks for biomedical named entity recognition. BMC Bioinformatics. 2019; 20(Suppl 10):249. https:\/\/doi.org\/10.1186\/s12859-019-2813-6.","journal-title":"BMC Bioinformatics"},{"key":"1216_CR13","volume-title":"Natural Language Processing and Chinese Computing","author":"H Zhu","year":"2019","unstructured":"Zhu H, Hu W, Zeng Y. Flexner: A flexible lstm-cnn stack framework for named entity recognition In: Tang J, Kan M-Y, Zhao D, Li S, Zan H, editors. Natural Language Processing and Chinese Computing. Cham: Springer: 2019. p. 168\u201378."},{"issue":"22","key":"1216_CR14","doi-asserted-by":"publisher","first-page":"2909","DOI":"10.1093\/bioinformatics\/btt474","volume":"29","author":"R Leaman","year":"2013","unstructured":"Leaman R, Islamaj Do\u011fan R, Lu Z. DNorm: disease name normalization with pairwise learning to rank. Bioinformatics. 2013; 29(22):2909\u201317. https:\/\/doi.org\/10.1093\/bioinformatics\/btt474.","journal-title":"Bioinformatics"},{"issue":"18","key":"1216_CR15","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1093\/bioinformatics\/btw343","volume":"32","author":"R Leaman","year":"2016","unstructured":"Leaman R, Lu Z. TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics. 2016; 32(18):2839\u201346. https:\/\/doi.org\/10.1093\/bioinformatics\/btw343.","journal-title":"Bioinformatics"},{"issue":"15","key":"1216_CR16","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1093\/bioinformatics\/btx172","volume":"33","author":"Y Lou","year":"2017","unstructured":"Lou Y, Zhang Y, Qian T, Li F, Xiong S, Ji D. A transition-based joint model for disease named entity recognition and normalization. Bioinformatics. 2017; 33(15):2363\u201371. https:\/\/doi.org\/10.1093\/bioinformatics\/btx172.","journal-title":"Bioinformatics"},{"key":"1216_CR17","doi-asserted-by":"crossref","unstructured":"Zhao S, Liu T, Zhao S, Wang F. A neural multi-task learning framework to jointly model medical named entity recognition and normalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33: 2019. p. 817\u201324.","DOI":"10.1609\/aaai.v33i01.3301817"},{"issue":"2","key":"1216_CR18","first-page":"287","volume":"3","author":"H Duan","year":"2011","unstructured":"Duan H, Zheng Y. A study on features of the crfs-based chinese named entity recognition. Int J Adv Intell. 2011; 3(2):287\u201394.","journal-title":"Int J Adv Intell"},{"key":"1216_CR19","doi-asserted-by":"crossref","unstructured":"Luo Y, Song G, Li P, Qi Z. Multi-task medical concept normalization using multi-view convolutional neural network. In: AAAI: 2018. p. 5868\u201375.","DOI":"10.1609\/aaai.v32i1.12060"},{"key":"1216_CR20","doi-asserted-by":"publisher","unstructured":"Zhang Y, Ma X, Song G. Chinese medical concept normalization by using text and comorbidity network embedding. 2018 IEEE International Conference on Data Mining (ICDM).2018. p. 777\u201386. https:\/\/doi.org\/10.1109\/ICDM.2018.00093.","DOI":"10.1109\/ICDM.2018.00093"},{"issue":"99","key":"1216_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TII.2018.2794987","volume":"PP","author":"Q Zhang","year":"2018","unstructured":"Zhang Q, Yang LT, Zheng Y, Chen Z, Peng L. An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Ind Inform. 2018; PP(99):1\u20131.","journal-title":"IEEE Trans Ind Inform"},{"key":"1216_CR22","unstructured":"Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint. 2018."},{"key":"1216_CR23","unstructured":"Dauphin YN, Fan A, Auli M, Grangier D. Language modeling with gated convolutional networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML\u201917: 2017. p. 933\u201341."},{"issue":"11","key":"1216_CR24","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. 1997; 45(11):2673\u201381.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"1216_CR25","unstructured":"Shen Y, Tan S, Sordoni A, Courville A. Ordered neurons: Integrating tree structures into recurrent neural networks. The International Conference on Learning Representations (ICLR).2019. p. 1\u201314."},{"key":"1216_CR26","unstructured":"Lafferty JD, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML: 2001. p. 282\u2013289."},{"key":"1216_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conf Comput Vis Pattern Recognit (CVPR).2016. p. 770\u201378.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1216_CR28","first-page":"1","volume":"abs\/1805.06334","author":"L Liebel","year":"2018","unstructured":"Liebel L, K\u00f6rner M. Auxiliary tasks in multi-task learning. ArXiv. 2018; abs\/1805.06334:1\u20138.","journal-title":"ArXiv"},{"key":"1216_CR29","doi-asserted-by":"publisher","unstructured":"Parthasarathy S, Busso C. Ladder networks for emotion recognition: Using unsupervised auxiliary tasks to improve predictions of emotional attributes. In: INTERSPEECH: 2018. https:\/\/doi.org\/10.21437\/Interspeech.2018-1391.","DOI":"10.21437\/Interspeech.2018-1391"},{"key":"1216_CR30","unstructured":"Shen Y, Tan S, Sordoni A, Courville A. Ordered neurons: Integrating tree structures into recurrent neural networks. In: International Conference on Learning Representations: 2019."},{"key":"1216_CR31","unstructured":"TensorFlow addons optimizers: LazyAdam. 2019. https:\/\/www.tensorflow.org\/addons\/tutorials\/optimizers\\_lazyadam."},{"key":"1216_CR32","unstructured":"Keras. 2019. https:\/\/github.com\/keras-team\/keras."},{"key":"1216_CR33","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X. Tensorflow: A system for large-scale machine learning. In: OSDI 16: 2016. p. 265\u201383."},{"key":"1216_CR34","doi-asserted-by":"publisher","unstructured":"Liu Y, Zhou Y, Wen S, Tang C. A strategy on selecting performance metrics for classifier evaluation. Int J Mob Comput Multimed Commun; 6(4):20\u201335. https:\/\/doi.org\/10.4018\/IJMCMC.2014100102.","DOI":"10.4018\/IJMCMC.2014100102"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01216-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-020-01216-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01216-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T18:30:44Z","timestamp":1668105044000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01216-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,28]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1216"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01216-9","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,28]]},"assertion":[{"value":"20 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The usage of clinical text from the real-world EHR dataset was approved by the Ethics Committee of the First Hospital of Lanzhou University (No.LDYYLL2019-274). The study was conducted according to the principles of the Declaration of Helsinki. In addition, the employed clinical data were all previously approved by the patients verbal consent, and the desensitized clinical text did not deal with any personally identifiable data that reflects an individual\u2019s identity.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"204"}}