{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:45:57Z","timestamp":1780418757650,"version":"3.54.1"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as \u201csymptom-disease\u201d, \u201claboratory examination\/imaging examination-disease\u201d, and \u201cdisease-treatment method\u201d. The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph. In this work, we describe a method for extracting medical entities using real Chinese clinical electronic clinical records. We define a computational architecture named MLEE to extract object-level entities with \u201cobject-attribute\u201d dependencies. We conducted experiments based on randomly selected electronic clinical records of 1,000 patients from Shengjing Hospital of China Medical University to verify the effectiveness of the method.<\/jats:p>","DOI":"10.3389\/fgene.2022.900242","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T11:12:02Z","timestamp":1658488322000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records"],"prefix":"10.3389","volume":"13","author":[{"given":"Genghong","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjian","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiren","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s40264-018-0762-z","article-title":"Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (made 1.0)","volume":"42","author":"Abhyuday","year":"2020","journal-title":"Drug Saf."},{"key":"B2","doi-asserted-by":"publisher","first-page":"70624","DOI":"10.1109\/ACCESS.2019.2919121","article-title":"A hybrid method to extract clinical information from Chinese electronic medical records","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"B3","volume-title":"Bert: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"B4","doi-asserted-by":"publisher","first-page":"261\u2013268","DOI":"10.11896\/j.issn.1002-137X.2018.02.045","article-title":"Named entity recognition method based on blstm","volume":"45","author":"Feng","year":"2018","journal-title":"Comput. 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