{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:13:50Z","timestamp":1780766030787,"version":"3.54.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"S7","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["U1813215"],"award-info":[{"award-number":["U1813215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["61876052"],"award-info":[{"award-number":["61876052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["61573118"],"award-info":[{"award-number":["61573118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2017YFB0802204"],"award-info":[{"award-number":["2017YFB0802204"]}]},{"name":"Special Foundation for Technology Research Program of Guangdong Province","award":["2015B010131010"],"award-info":[{"award-number":["2015B010131010"]}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019A1515011158"],"award-info":[{"award-number":["2019A1515011158"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Emerging Industry Development Special Funds of Shenzhen","award":["JCYJ20180306172232154"],"award-info":[{"award-number":["JCYJ20180306172232154"]}]},{"name":"Innovation Fund of Harbin Institute of Technology","award":["HIT.NSRIF.2017052"],"award-info":[{"award-number":["HIT.NSRIF.2017052"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>Relation extraction (RE) is a fundamental task of natural language processing, which always draws plenty of attention from researchers, especially RE at the document-level. We aim to explore an effective novel method for document-level medical relation extraction.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose a novel edge-oriented graph neural network based on document structure and external knowledge for document-level medical RE, called SKEoG. This network has the ability to take full advantage of document structure and external knowledge.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We evaluate SKEoG on two public datasets, that is, Chemical-Disease Relation (CDR) dataset and Chemical Reactions dataset (CHR) dataset, by comparing it with other state-of-the-art methods. SKEoG achieves the highest F1-score of 70.7 on the CDR dataset and F1-score of 91.4 on the CHR dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The proposed SKEoG method achieves new state-of-the-art performance. Both document structure and external knowledge can bring performance improvement in the EoG framework. Selecting proper methods for knowledge node representation is also very important.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01733-1","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T09:02:22Z","timestamp":1640854942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge"],"prefix":"10.1186","volume":"21","author":[{"given":"Tao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingcai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-8246","authenticated-orcid":false,"given":"Buzhou","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"1733_CR1","unstructured":"Socher R, Huval B, Manning CD, Ng AY. Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Jeju Island, Korea, July 2012. p. 1201\u201311. Accessed 16 Oct 2020 [Online]."},{"key":"1733_CR2","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J. Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, Dublin, Ireland, Aug 2014. p. 2335\u201344. Accessed 16 Oct 2020 [Online]."},{"key":"1733_CR3","doi-asserted-by":"publisher","unstructured":"Gu J, Sun F, Qian L, Zhou G. Chemical-induced disease relation extraction via convolutional neural network. Database. 2017. https:\/\/doi.org\/10.1093\/database\/bax024.","DOI":"10.1093\/database\/bax024"},{"key":"1733_CR4","doi-asserted-by":"crossref","unstructured":"Verga P, Strubell E, McCallum A. Simultaneously self-attending to all mentions for full-abstract biological relation extraction. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, New Orleans, Louisiana, Jun 2018, vol. 1. p. 872\u201384. Accessed 26 Oct 2020 [Online].","DOI":"10.18653\/v1\/N18-1080"},{"key":"1733_CR5","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Verspoor K. Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings. In: Proceedings of the BioNLP 2018 workshop, Melbourne, Australia, 2018. p. 129\u201336.","DOI":"10.18653\/v1\/W18-2314"},{"key":"1733_CR6","doi-asserted-by":"crossref","unstructured":"Zeng D, Liu K, Chen Y, Zhao J. Distant supervision for relation extraction via piecewise convolutional neural networks. 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