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MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records

As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as “symptom-disease”, “laboratory examination/imaging examination-disease”, and “disease-treatment method”. The continuous expansion of large electronic clini...

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Detalles Bibliográficos
Autores principales: Zhao, Genghong, Gu, Wenjian, Cai, Wei, Zhao, Zhiying, Zhang, Xia, Liu, Jiren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354090/
https://www.ncbi.nlm.nih.gov/pubmed/35938002
http://dx.doi.org/10.3389/fgene.2022.900242
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author Zhao, Genghong
Gu, Wenjian
Cai, Wei
Zhao, Zhiying
Zhang, Xia
Liu, Jiren
author_facet Zhao, Genghong
Gu, Wenjian
Cai, Wei
Zhao, Zhiying
Zhang, Xia
Liu, Jiren
author_sort Zhao, Genghong
collection PubMed
description As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as “symptom-disease”, “laboratory examination/imaging examination-disease”, and “disease-treatment method”. 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 “object-attribute” 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.
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spelling pubmed-93540902022-08-06 MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records Zhao, Genghong Gu, Wenjian Cai, Wei Zhao, Zhiying Zhang, Xia Liu, Jiren Front Genet Genetics As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as “symptom-disease”, “laboratory examination/imaging examination-disease”, and “disease-treatment method”. 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 “object-attribute” 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. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9354090/ /pubmed/35938002 http://dx.doi.org/10.3389/fgene.2022.900242 Text en Copyright © 2022 Zhao, Gu, Cai, Zhao, Zhang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhao, Genghong
Gu, Wenjian
Cai, Wei
Zhao, Zhiying
Zhang, Xia
Liu, Jiren
MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title_full MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title_fullStr MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title_full_unstemmed MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title_short MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records
title_sort mlee: a method for extracting object-level medical knowledge graph entities from chinese clinical records
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354090/
https://www.ncbi.nlm.nih.gov/pubmed/35938002
http://dx.doi.org/10.3389/fgene.2022.900242
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