{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:24:19Z","timestamp":1779315859875,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"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":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper studies the extraction of information from unstructured text data of medical literature and electronic medical records in the field of medicine, and proposes a TCM-KR method of knowledge reasoning based on electronic medical records to enhance association rules, and carries out a study on association characteristics in the field of the electronic medical record. This method abstracts the word bag representation mode of text semantics from the unstructured data representation and integrates the correlation information of the knowledge graph of the medicine domain. The method based on a graph convolutional network was used to predict the unknown associations' relations between viscera, channel tropism, and channel distribution. The experimental results show that the TCM-KR method can efficiently infer a large amount of high-quality triple knowledge from the unstructured text data of medicine, and predict the correlation characteristics of Syndromes-Viscera, Chinese medicinal-Channel tropism, Acupoints-Channel distribution in treating lumbar intervertebral disc prolapse and provide a dedicated machine learning model and guidance for clinical diagnosis and treatment.<\/jats:p>","DOI":"10.1007\/s44196-023-00219-4","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T11:02:52Z","timestamp":1679482972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on the Medical Knowledge Deduction Based on the Semantic Relevance of Electronic Medical Record"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhi","family":"Qiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guibin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"issue":"12","key":"219_CR1","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., et al.: Knowledge graph embedding: A survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"219_CR2","doi-asserted-by":"crossref","unstructured":"Li, Q., Huang, L. F., Zhong, J., et al.: Data-driven discovery of a sepsis patients severity prediction in the ICU via pre-training BiLSTM Networks. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp. 668\u2013673 (2019)","DOI":"10.1109\/BIBM47256.2019.8983197"},{"issue":"1","key":"219_CR3","first-page":"105","volume":"4","author":"W Lu","year":"2015","unstructured":"Lu, W., Wu, C.: Literature review on entity linking. Technol Intell Engineering 4(1), 105\u2013112 (2015)","journal-title":"Technol Intell Engineering"},{"key":"219_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jep.2021.113957","volume":"272","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Li, L., Song, M., et al.: Evaluating the traditional Chinese medicine (TCM) officially recommended in China for COVID-19 using ontology-based side-effect prediction framework (OSPF) and deep learning. J. Ethnopharmacol. 272, 113957 (2021)","journal-title":"J. Ethnopharmacol."},{"issue":"10","key":"219_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78\u201385 (2014)","journal-title":"Commun. ACM"},{"key":"219_CR6","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"219_CR7","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp. 1247-1250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"219_CR8","doi-asserted-by":"crossref","unstructured":"Liu, L., Ren, X., Zhu, Q., et al.: Heterogeneous supervision for relation extraction: a representation learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 46\u201356 (2017)","DOI":"10.18653\/v1\/D17-1005"},{"issue":"1","key":"219_CR9","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.spinee.2013.08.003","volume":"14","author":"DS Kreiner","year":"2014","unstructured":"Kreiner, D.S., Hwang, S.W., Easa, J.E., et al.: An evidence based clinical guideline for the diagnosis and treatment of lumbar disc herniation with radiculopathy. Spine J. 14(1), 180\u2013191 (2014). https:\/\/doi.org\/10.1016\/j.spinee.2013.08.003","journal-title":"Spine J."},{"issue":"2","key":"219_CR10","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.pjnns.2015.12.001","volume":"50","author":"D Latka","year":"2016","unstructured":"Latka, D., Miekisiak, G., Jarmuzek, P., et al.: Treatment of lumbar disc herniation with radiculopathy. Clinical practice guidelines endorsed by The Polish Society of Spinal Surgery. Neurol. Neurochir. Pol. 50(2), 101\u2013108 (2016). https:\/\/doi.org\/10.1016\/j.pjnns.2015.12.001","journal-title":"Neurol. Neurochir. Pol."},{"issue":"5","key":"219_CR11","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1590\/1806\u20439282.64.05.397","volume":"64","author":"AF Joaquim","year":"2018","unstructured":"Joaquim, A.F., Botelho, R.V., Mudo, M.L., et al.: Lumbar herniated discendoscopic discectomy treatment. Rev. Assoc. Med. Bras. 64(5), 397\u2013407 (2018). https:\/\/doi.org\/10.1590\/1806\u20439282.64.05.397","journal-title":"Rev. Assoc. Med. Bras."},{"issue":"18","key":"219_CR12","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1056\/NEJMcp1512658","volume":"374","author":"RA Deyo","year":"2016","unstructured":"Deyo, R.A., Mirza, S.K.: Clinical practice. Herniated lumbar intervertebral disk. N. Engl. J. Med. 374(18), 1763\u20131772 (2016). https:\/\/doi.org\/10.1056\/NEJMcp1512658","journal-title":"N. Engl. J. Med."},{"issue":"2","key":"219_CR13","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/s1297\u2043319x(02)00385-8","volume":"69","author":"M Benoist","year":"2002","unstructured":"Benoist, M.: The natural history of lumbar disc herniation and radiculopathy. J. Bone Spine 69(2), 155\u2013160 (2002). https:\/\/doi.org\/10.1016\/s1297\u2043319x(02)00385-8","journal-title":"J. Bone Spine"},{"issue":"2","key":"219_CR14","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1097\/00007632\u2043199701150\u204300015","volume":"8","author":"H Weber","year":"1983","unstructured":"Weber, H.: Lumbar disc herniation. A controlled, prospective study with ten years of observation. Spine (Phila Pa 1976) 8(2), 131\u2013140 (1983). https:\/\/doi.org\/10.1097\/00007632\u2043199701150\u204300015","journal-title":"Spine (Phila Pa 1976)"},{"key":"219_CR15","doi-asserted-by":"publisher","first-page":"e497","DOI":"10.1136\/bmj.e497","volume":"344","author":"RZ Pinto","year":"2012","unstructured":"Pinto, R.Z., Maher, C.G., Ferreira, M.L., et al.: Drugs for relief of pain in patients with sciatica: systematic review and meta-analysis. BMJ 344, e497 (2012). https:\/\/doi.org\/10.1136\/bmj.e497","journal-title":"BMJ"},{"issue":"17","key":"219_CR16","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1056\/NEJM198610233151705","volume":"315","author":"RA Deyo","year":"1986","unstructured":"Deyo, R.A., Diehl, A.K., Rosenthal, M.: How many days of bed rest for acute low back pain? A randomized clinical trial. N. Engl. J. Med. 315(17), 1064\u20131070 (1986). https:\/\/doi.org\/10.1056\/NEJM198610233151705","journal-title":"N. Engl. J. Med."},{"key":"219_CR17","doi-asserted-by":"publisher","first-page":"6821310","DOI":"10.1155\/2019\/6821310","volume":"2019","author":"Z Mo","year":"2019","unstructured":"Mo, Z., Li, D., Zhang, R., et al.: Comparisons of the effectiveness and safety of Tuina, acupuncture, traction, and Chinese herbs for lumbar disc herniation: asystematic review and network meta\u2043analysis. Evid. Based Compl. Alternat. Med. 2019, 6821310 (2019). https:\/\/doi.org\/10.1155\/2019\/6821310","journal-title":"Evid. Based Compl. Alternat. Med."},{"key":"219_CR18","first-page":"652","volume-title":"National CCF Conference on Natural Language Processing and Chinese Computing","author":"X Yang","year":"2017","unstructured":"Yang, X., Ren, S., Li, Y., et al.: Relation linking for wikidata using bag of distribution representation. In: National CCF Conference on Natural Language Processing and Chinese Computing, pp. 652\u2013661. Springer, Cham (2017)"},{"key":"219_CR19","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., et al.: Translating embeddings for modeling mult-I relational data. In: Advances in Neural Information Processing Systems, pp. 2787\u20132795 (2013)"},{"key":"219_CR20","first-page":"1112","volume":"14","author":"Z Wang","year":"2014","unstructured":"Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. AAAI 14, 1112\u20131119 (2014)","journal-title":"AAAI"},{"key":"219_CR21","unstructured":"Qing, L., Guanzhong, W., Tao, Y., et al.: BioELM: Integrating Biomedical Knowledge into Language Model with Entity-Linking. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE (2022)"},{"issue":"6","key":"219_CR22","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","volume":"24","author":"L Gal\u00e1rraga","year":"2015","unstructured":"Gal\u00e1rraga, L., Teflioudi, C., Hose, K., et al.: Fast rule mining in ontological knowledge bases with AMIE. Itnt. J. Very Large Data Bases 24(6), 707\u2013730 (2015)","journal-title":"Itnt. J. Very Large Data Bases"},{"key":"219_CR23","unstructured":"Wang, Z., Li, J.: RDF2Rules: learning rules from RDF knowledge bases by mining frequent predicate cycles [DB\/OL]. [201 8\u201308\u201320]. https:\/\/arxiv.org\/abs\/1512.07734 (2015)"},{"key":"219_CR24","doi-asserted-by":"crossref","unstructured":"Zeng, D, Liu, K., Chen, Y., et al.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753-1762 (2015)","DOI":"10.18653\/v1\/D15-1203"},{"key":"219_CR25","unstructured":"Lin, Y., Shen, S., Liu, Z., et al.: Neural relation extraction with selective attention over instances. In: Proceedings of the 5 4th Annual Meeting of the Association for Computational Linguistics, pp. 2124\u20132133 (2016)"},{"key":"219_CR26","first-page":"402","volume":"1","author":"Q Li","year":"2014","unstructured":"Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. Proc. Meet. Assoc. Comput. Linguist. 1, 402\u2013412 (2014)","journal-title":"Proc. Meet. Assoc. Comput. Linguist."},{"key":"219_CR27","doi-asserted-by":"crossref","unstructured":"Miwa, M., Sasaki, Y.: Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1858\u20131869 (2014)","DOI":"10.3115\/v1\/D14-1200"},{"key":"219_CR28","unstructured":"Ren, X., Wu, Z., He, W., et al.: Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 1015\u20131024 (2014)"},{"key":"219_CR29","unstructured":"Ramanan, S., Nathan, PS.: Performance and limitations of the linguistically motivated cocoa\/peabcrry system in a broad biological domain. In: Proceedings of the BioNLP Shared Task 2013 Workshop, pp. 86\u201393 (2013)"},{"issue":"16","key":"219_CR30","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1186\/1471-2105-16-S16-S2","volume":"16","author":"H Liu","year":"2015","unstructured":"Liu, H., Verspoor, K., Comeau, D.C., et al.: Optimizing graph\u2014based patterns to extract biomedical events from the literature. BMC Bioinf. 16(16), S2 (2015)","journal-title":"BMC Bioinf."},{"issue":"1","key":"219_CR31","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.artmed.2015.03.004","volume":"64","author":"D Zhou","year":"2015","unstructured":"Zhou, D., Zhong, D.: A semi-supervised learning framework for biomedical event extraction based on hidden topics. Artif. Intell. Med. 64(1), 51\u201358 (2015)","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"219_CR32","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1111\/j.1467-8640.2011.00399.x","volume":"27","author":"J Bjorne","year":"2011","unstructured":"Bjorne, J., Heimonen, J., Ginter, F., et al.: Extracting contextualized complex biological events with rich graph-based feature sets. Comput. Intell. 27(4), 541\u2013557 (2011)","journal-title":"Comput. Intell."},{"issue":"16","key":"219_CR33","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1471-2105-16-S16-S4","volume":"16","author":"J Bjorne","year":"2015","unstructured":"Bjorne, J., Salakoski, T.: Tees 2.2: biomedical event extraction for diverse corpora. BMC Bioinf. 16(16), S4 (2015)","journal-title":"BMC Bioinf."},{"issue":"10","key":"219_CR34","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1186\/1471-2105-16-S10-S7","volume":"16","author":"M Miwa","year":"2015","unstructured":"Miwa, M., Ananiadou, S.: Adaptable, high recall, event extraction system with minimal configuration. BMC Bioinf. 16(10), S7 (2015)","journal-title":"BMC Bioinf."},{"key":"219_CR35","doi-asserted-by":"crossref","unstructured":"Bjorne, J., Salakoski, T.: Biomedical event extraction using convolutional neural networks and dependency parsing. In: Proceedings of the BioNLP 2018 workshop, pp. 98\u2013108 (2018)","DOI":"10.18653\/v1\/W18-2311"},{"issue":"3","key":"219_CR36","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1186\/s12911-017-0563-9","volume":"17","author":"A Wang","year":"2017","unstructured":"Wang, A., Wang, J., Lin, H., et al.: A multiple distributed representation method based on neural network for biomedical event extraction. BMC Med. Inform. Decis. Mak. 17(3), 171 (2017)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"219_CR37","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.ins.2022.10.063","volume":"617","author":"L Qing","year":"2022","unstructured":"Qing, L., Yichen, W., Tao, Y.: BioKnowPrompt: incorporating knowledge into prompt-tuning verbalizer with biomedical text for relation extraction. Inf. Sci. 617, 346\u2013358 (2022)","journal-title":"Inf. Sci."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-023-00219-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-023-00219-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-023-00219-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T11:17:18Z","timestamp":1679483838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-023-00219-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["219"],"URL":"https:\/\/doi.org\/10.1007\/s44196-023-00219-4","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]},"assertion":[{"value":"13 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"No animal or human studies are presented in this manuscript,no potentially identifiable human images or data is presented in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"All authors agree to publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"38"}}