{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:08:33Z","timestamp":1742980113326,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819770069"},{"type":"electronic","value":"9789819770076"}],"license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-7007-6_2","type":"book-chapter","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:01:43Z","timestamp":1726941703000},"page":"17-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ESert: An Enhanced Span-Based Model for\u00a0Measurable Quantitative Information Extraction from\u00a0Medical Texts"],"prefix":"10.1007","author":[{"given":"Qixuan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jiale","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Jie","sequence":"additional","affiliation":[]},{"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"2_CR1","unstructured":"Evans, D.A., Brownlow, N.D., Hersh, W.R., Campbell, E.M.: Automating concept identification in the electronic medical record: an experiment in extracting dosage information. In: Proceedings of the AMIA Annual Fall Symposium, p.\u00a0388. American Medical Informatics Association (1996)"},{"issue":"5","key":"2_CR2","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.amjmed.2016.12.008","volume":"130","author":"DW Frost","year":"2017","unstructured":"Frost, D.W., Vembu, S., Wang, J., Tu, K., Morris, Q., Abrams, H.B.: Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs. Am. J. Med. 130(5), 601-e17 (2017)","journal-title":"Am. J. Med."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Maguire, A., Johnson, M.E., Denning, D.W., Ferreira, G.L., Cassidy, A.: Identifying rare diseases using electronic medical records: the example of allergic bronchopulmonary aspergillosis. Pharmacoepidemiol. Drug Safety 26(7), 785\u2013791 (2017)","DOI":"10.1002\/pds.4204"},{"key":"2_CR4","unstructured":"Hao, T., Wang, H., Cao, X., Lee, K.: Annotating measurable quantitative informationin language: for an ISO standard. In: Proceedings 14th Joint ACL-ISO Workshop on Interoperable Semantic Annotation, pp. 69\u201375 (2018)"},{"key":"2_CR5","unstructured":"Hao, T., We, Y., Qiang, J., Wang, H., Lee, K.: The representation and extraction of qunatitative information. In: Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13) (2017)"},{"key":"2_CR6","unstructured":"Hao, T., Wang, H.: Semantic annotation framework (SemAF)-part 11: measurable quantitative information (MQI). ISO\/DIS 24617-11. International Organization for Standardization (2019)"},{"issue":"5","key":"2_CR7","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/j.jbi.2009.07.007","volume":"42","author":"A Mykowiecka","year":"2009","unstructured":"Mykowiecka, A., Marciniak, M., Kup\u015b\u0107, A.: Rule-based information extraction from patients\u2019 clinical data. J. Biomed. Inform. 42(5), 923\u2013936 (2009)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"2_CR8","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1197\/jamia.M3378","volume":"17","author":"H Xu","year":"2010","unstructured":"Xu, H., Stenner, S.P., Doan, S., Johnson, K.B., Waitman, L.R., Denny, J.C.: MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inform. Assoc. 17(1), 19\u201324 (2010)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"e1","key":"2_CR9","doi-asserted-by":"publisher","first-page":"e40","DOI":"10.1093\/jamia\/ocw097","volume":"24","author":"SM Meystre","year":"2017","unstructured":"Meystre, S.M., et al.: Congestive heart failure information extraction framework for automated treatment performance measures assessment. J. Am. Med. Inform. Assoc. 24(e1), e40\u2013e46 (2017)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Chen, G., et al.: Improving open intent detection via triplet-contrastive learning and adaptive boundary. IEEE Trans. Consum. Electron. (2024)","DOI":"10.1109\/TCE.2024.3363896"},{"issue":"6","key":"2_CR11","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1197\/jamia.M2078","volume":"13","author":"A Turchin","year":"2006","unstructured":"Turchin, A., Kolatkar, N.S., Grant, R.W., Makhni, E.C., Pendergrass, M.L., Einbinder, J.S.: Using regular expressions to abstract blood pressure and treatment intensification information from the text of physician notes. J. Am. Med. Inform. Assoc. 13(6), 691\u2013695 (2006)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Murata, M., et al.: Sophisticated text mining system for extracting and visualizing numerical and named entity information from a large number of documents. In: NTCIR (2008)","DOI":"10.1109\/NLPKE.2008.4906795"},{"issue":"03","key":"2_CR13","doi-asserted-by":"publisher","first-page":"266","DOI":"10.3414\/ME15-01-0112","volume":"55","author":"T Hao","year":"2016","unstructured":"Hao, T., Liu, H., Weng, C.: Valx: a system for extracting and structuring numeric lab test comparison statements from text. Methods Inf. Med. 55(03), 266\u2013275 (2016)","journal-title":"Methods Inf. Med."},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-01078-2_9","volume-title":"Health Information Science","author":"S Liu","year":"2018","unstructured":"Liu, S., Pan, X., Chen, B., Gao, D., Hao, T.: An automated approach for clinical quantitative information extraction from Chinese electronic medical records. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds.) HIS 2018. LNCS, vol. 11148, pp. 98\u2013109. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01078-2_9"},{"key":"2_CR15","unstructured":"Hundman, K., Mattmann, C.A.: Measurement context extraction from text: discovering opportunities and gaps in earth science. arXiv preprint arXiv:1710.04312 (2017)"},{"key":"2_CR16","unstructured":"Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Brodley, C.E., Danyluk, A.P. (eds.) Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pp. 282\u2013289. Morgan Kaufmann (2001)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Tang, B., Cao, H., Wu, Y., Jiang, M., Xu, H.: Clinical entity recognition using structural support vector machines with rich features. In: Proceedings of the ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics, pp. 13\u201320 (2012)","DOI":"10.1145\/2390068.2390073"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.dss.2018.07.004","volume":"113","author":"R Gruss","year":"2018","unstructured":"Gruss, R., Abrahams, A.S., Fan, W., Wang, G.A.: By the numbers: the magic of numerical intelligence in text analytic systems. Decis. Support Syst. 113, 86\u201398 (2018)","journal-title":"Decis. Support Syst."},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0933-6","volume":"19","author":"L Li","year":"2019","unstructured":"Li, L., Zhao, J., Hou, L., Zhai, Y., Shi, J., Cui, F.: An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records. BMC Med. Inform. Decis. Mak. 19, 1\u201311 (2019)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"2_CR20","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":"2_CR21","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"2_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.103985","volume":"132","author":"X Zhang","year":"2019","unstructured":"Zhang, X., et al.: Extracting comprehensive clinical information for breast cancer using deep learning methods. Int. J. Med. Informatics 132, 103985 (2019)","journal-title":"Int. J. Med. Informatics"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Mo, D., et al.: SCLert: a span-based joint model for measurable quantitative information extraction from Chinese texts. IEEE Trans. Consum. Electron. 70, 3361\u20133371 (2023)","DOI":"10.1109\/TCE.2023.3327681"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"2_CR25","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Ott, M., et al.: fairseq: a fast, extensible toolkit for sequence modeling. arXiv preprint arXiv:1904.01038 (2019)","DOI":"10.18653\/v1\/N19-4009"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Xie, X., et al.: Zjuklab at semeval-2021 task 4: Negative augmentation with language model for reading comprehension of abstract meaning. arXiv preprint arXiv:2102.12828 (2021)","DOI":"10.18653\/v1\/2021.semeval-1.108"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Revisiting and advancing Chinese natural language understanding with accelerated heterogeneous knowledge pre-training. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 560\u2013570 (2022)","DOI":"10.18653\/v1\/2022.emnlp-industry.57"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s13042-020-01160-0","volume":"12","author":"S Liu","year":"2021","unstructured":"Liu, S., Nie, W., Gao, D., Yang, H., Yan, J., Hao, T.: Clinical quantitative information recognition and entity-quantity association from Chinese electronic medical records. Int. J. Mach. Learn. Cybern. 12, 117\u2013130 (2021)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15-20 July 2018, Volume 1: Long Papers, pp. 1554\u20131564. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/V1\/P18-1144, https:\/\/aclanthology.org\/P18-1144\/","DOI":"10.18653\/V1\/P18-1144"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y.G., Huang, X.: CNN-based Chinese NER with lexicon rethinking. In: ijcai, vol.\u00a02019 (2019)","DOI":"10.24963\/ijcai.2019\/692"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: ChineseBERT: Chinese pretraining enhanced by glyph and pinyin information. arXiv preprint arXiv:2106.16038 (2021)","DOI":"10.18653\/v1\/2021.acl-long.161"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Su, H., Shi, W., Shen, X., Xiao, Z., Ji, T., Fang, J., Zhou, J.: RoCBert: robust Chinese BERT with multimodal contrastive pretraining. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 921\u2013931 (2022)","DOI":"10.18653\/v1\/2022.acl-long.65"},{"key":"2_CR34","unstructured":"He, P., Gao, J., Chen, W.: DeBERTaV3: improving DeBERTa using electra-style pre-training with gradient-disentangled embedding sharing. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"2_CR35","doi-asserted-by":"publisher","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., Chang, Y.: A novel cascade binary tagging framework for relational triple extraction. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5-10 July 2020, pp. 1476\u20131488. Association for Computational Linguistics (2020).https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.136","DOI":"10.18653\/V1\/2020.ACL-MAIN.136"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Sui, D., Zeng, X., Chen, Y., Liu, K., Zhao, J.: Joint entity and relation extraction with set prediction networks. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3264735"},{"key":"2_CR37","unstructured":"Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. In: ECAI 2020, pp. 2006\u20132013. IOS Press (2020)"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7007-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:03:58Z","timestamp":1726941838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7007-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"ISBN":["9789819770069","9789819770076"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7007-6_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024,9,22]]},"assertion":[{"value":"22 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aaci.org.hk\/ncaa2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}