{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:25:59Z","timestamp":1743045959505,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030630300"},{"type":"electronic","value":"9783030630317"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63031-7_32","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:05:35Z","timestamp":1605139535000},"page":"444-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph"],"prefix":"10.1007","author":[{"given":"Kunli","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Hongying","family":"Zan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","unstructured":"Baker, S., Korhonen, A.: Initializing neural networks for hierarchical multi-label text classification. In: BioNLP 2017, pp. 307\u2013315. Association for Computational Linguistics, Vancouver, Canada, August 2017. https:\/\/doi.org\/10.18653\/v1\/W17-2339, https:\/\/www.aclweb.org\/anthology\/W17-2339","DOI":"10.18653\/v1\/W17-2339"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Chen, G., Ye, D., Xing, Z., Chen, J., Cambria, E.: Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2377\u20132383. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966144"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Hu, Y., Liu, J., Xiao, Y., Jiang, H.: Deep short text classification with knowledge powered attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 6252\u20136259 (2019)","DOI":"10.1609\/aaai.v33i01.33016252"},{"key":"32_CR4","unstructured":"China\u2019s Ministry of Health: Basic specification of electronic medical records (trial). Technical Report 3 (2010)"},{"key":"32_CR5","unstructured":"Cui, Y., et al.: Pre-training with whole word masking for chinese bert. arXiv preprint arXiv:1906.08101 (2019)"},{"key":"32_CR6","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/www.aclweb.org\/anthology\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Gai, R.L., Gao, F., Duan, L.M., Sun, X.H., Li, H.Z.: Bidirectional maximal matching word segmentation algorithm with rules. In: Advanced Materials Research, vol. 926, pp. 3368\u20133372. Trans Tech Publ. (2014)","DOI":"10.4028\/www.scientific.net\/AMR.926-930.3368"},{"key":"32_CR8","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64\u201377 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Kurata, G., Xiang, B., Zhou, B.: Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 521\u2013526 (2016)","DOI":"10.18653\/v1\/N16-1063"},{"key":"32_CR10","unstructured":"Li, M., Clinton, G., Miao, Y., Gao, F.: Short text classification via knowledge powered attention with similarity matrix based CNN. arXiv preprint arXiv:2002.03350 (2020)"},{"key":"32_CR11","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"issue":"5","key":"32_CR12","first-page":"128","volume":"32","author":"H Ma","year":"2018","unstructured":"Ma, H., Zhang, K., Zhao, Y.: Study on obstetric multi-label assisted diagnosis based on feature fusion. J. Chinese Inf. Process. 32(5), 128\u2013136 (2018)","journal-title":"J. Chinese Inf. Process."},{"key":"32_CR13","doi-asserted-by":"publisher","unstructured":"Ma, S., Sun, X., Wang, Y., Lin, J.: Bag-of-words as target for neural machine translation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 332\u2013338. Association for Computational Linguistics, Melbourne, Australia, July 2018. https:\/\/doi.org\/10.18653\/v1\/P18-2053, https:\/\/www.aclweb.org\/anthology\/P18-2053","DOI":"10.18653\/v1\/P18-2053"},{"key":"32_CR14","doi-asserted-by":"publisher","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227\u20132237. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-1202, https:\/\/www.aclweb.org\/anthology\/N18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"32_CR15","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Technical report, OpenAI (2018)"},{"issue":"3","key":"32_CR16","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)","journal-title":"Mach. Learn."},{"key":"32_CR17","unstructured":"Sun, Y., et al.: Ernie: enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223 (2019)"},{"issue":"7","key":"32_CR18","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TKDE.2010.164","volume":"23","author":"G Tsoumakas","year":"2010","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079\u20131089 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"32_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems, pp. 5998\u20136008 (2017)"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Yang, A., et al.: Enhancing pre-trained language representations with rich knowledge for machine reading comprehension. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2346\u20132357 (2019)","DOI":"10.18653\/v1\/P19-1226"},{"key":"32_CR21","unstructured":"Yang, H.l., Yang, Z.: Effect of older pregnancy on maternal and fetal outcomes. Chinese J. Obstetric Emergency (Electr. Edn) 5(3), 129\u2013135 (2016)"},{"key":"32_CR22","unstructured":"Yang, P., et al.: SGM: sequence generation model for multi-label classification, pp. 3915\u20133926 (2018)"},{"key":"32_CR23","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, pp. 5754\u20135764 (2019)"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, C., Duan, X., Zhou, L., Zhao, Y., Zan, H.: Bert with enhanced layer for assistant diagnosis based on chinese obstetric EMRS. In: 2019 International Conference on Asian Language Processing (IALP), pp. 384\u2013389. IEEE (2019)","DOI":"10.1109\/IALP48816.2019.9037721"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, K., Ma, H., Zhao, Y., Zan, H., Zhuang, L.: The comparative experimental study of multilabel classification for diagnosis assistant based on Chinese obstetric EMRS. J. Healthcare Eng. 2018 (2018)","DOI":"10.1155\/2018\/7273451"},{"issue":"10","key":"32_CR26","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","volume":"18","author":"ML Zhang","year":"2006","unstructured":"Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338\u20131351 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"32_CR27","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang, M.L., Zhou, Z.H.: Ml-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038\u20132048 (2007)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63031-7_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:05:44Z","timestamp":1710270344000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63031-7_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630300","9783030630317"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63031-7_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hainan","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cips-cl.org\/static\/CCL2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}