Literature DB >> 32175080

Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese.

Xin Li1, Haoyang Liu2, Xu Zhao3, Guigang Zhang4, Chunxiao Xing5.   

Abstract

In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database to construct the knowledge graph of knee osteoarthritis. The experiment proves that the knowledge graph is comprehensive and reliable, and the knowledge graph construction method proposed in this study is effective. © Springer Nature Switzerland AG 2020.

Entities:  

Keywords:  Electronic medical record; Entity recognition; Entity relation extraction; Knee osteoarthritis; Knowledge graph

Year:  2020        PMID: 32175080      PMCID: PMC7046853          DOI: 10.1007/s13755-020-0102-4

Source DB:  PubMed          Journal:  Health Inf Sci Syst        ISSN: 2047-2501


  6 in total

1.  The Unified Medical Language System (UMLS): integrating biomedical terminology.

Authors:  Olivier Bodenreider
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

2.  Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

Authors:  Guergana K Savova; James J Masanz; Philip V Ogren; Jiaping Zheng; Sunghwan Sohn; Karin C Kipper-Schuler; Christopher G Chute
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

3.  Semantic relations for problem-oriented medical records.

Authors:  Ozlem Uzuner; Jonathan Mailoa; Russell Ryan; Tawanda Sibanda
Journal:  Artif Intell Med       Date:  2010-06-19       Impact factor: 5.326

4.  A comprehensive study of named entity recognition in Chinese clinical text.

Authors:  Jianbo Lei; Buzhou Tang; Xueqin Lu; Kaihua Gao; Min Jiang; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2013-12-17       Impact factor: 4.497

5.  Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.

Authors:  Yan Xu; Yining Wang; Tianren Liu; Jiahua Liu; Yubo Fan; Yi Qian; Junichi Tsujii; Eric I Chang
Journal:  J Am Med Inform Assoc       Date:  2013-08-09       Impact factor: 4.497

6.  Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

Authors:  Berry de Bruijn; Colin Cherry; Svetlana Kiritchenko; Joel Martin; Xiaodan Zhu
Journal:  J Am Med Inform Assoc       Date:  2011-05-12       Impact factor: 4.497

  6 in total
  2 in total

1.  Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.

Authors:  Ting Liu; Xueli Pan; Xu Wang; K Anton Feenstra; Jaap Heringa; Zhisheng Huang
Journal:  Health Inf Sci Syst       Date:  2020-11-24

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

Authors:  Genghong Zhao; Wenjian Gu; Wei Cai; Zhiying Zhao; Xia Zhang; Jiren Liu
Journal:  Front Genet       Date:  2022-07-22       Impact factor: 4.772

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.