Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Li Peilin; Yuan* Zhenming; Tu Wenbo; Yu Kai; Lu Dongxin

doi:10.24920/003589

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Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Chinese Medical Sciences Journal;Chinese Medical Sciences Journal Vol. 34, Issue 2, Pages: 133-139(2019)

Original Article | Updated：2024-04-10

- Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning
- Affiliations：
  
  杭州师范大学移动健康管理系统教育部工程研究中心,杭州 311121,中国
- Author bio：
- Funds：
- DOI：10.24920/003589
  CLC：
- Received：29 March 2019，
  
  Accepted：2019-4-24，
  
  Published Online：14 May 2019，
  
  Published：14 May 2019
- Accepted：
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Li Peilin, Yuan* Zhenming, Tu Wenbo, et al. Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 133-139.
DOI：

Li Peilin, Yuan* Zhenming, Tu Wenbo, et al. Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 133-139. DOI： 10.24920/003589.

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Wu JW , Guan Y , Lv XB . Entity relation extraction from electronic medical records based on deep learning . Intell comput appl 2014 ; 4 ( 3 ): 35 - 8 . doi: DOI: 10.3969/j.issn.2095-2163.2014.03.009 http://doi.org/10.3969/j.issn.2095-2163.2014.03.009 .

Chen L , Li Y , Chen W , et al . Utilizing Soft Constraints to Enhance Medical Relation Extraction from the History of Present Illness in Electronic Medical Records . J Biomed Inform 2018 ; 87 : 108 - 17 . doi: . DOI: 10.1016/j.jbi.2018.09.013 http://doi.org/10.1016/j.jbi.2018.09.013 https://linkinghub.elsevier.com/retrieve/pii/S1532046418301862 https://linkinghub.elsevier.com/retrieve/pii/S1532046418301862

Xue NW , Shen LB . Chinese word segmentation as LMR tagging. Proceedings of the second SIGHAN workshop on Chinese language processing. 2003, Jul 11-12; Sapporo, Japan. Stroudsburg, PA, USA: Association for computation Linguistics ; 2003 . 17 : 176 - 9 . doi: DOI: 10.3115/1119250.1119278 http://doi.org/10.3115/1119250.1119278 .

Finkel JR , Grenager T , Manning C . Incorporating non-local information into information extraction systems by Gibbs sampling . Proceedings of the 43rd annual meeting on association for computational linguistics 2005 ; 363 - 70 . doi: DOI: 10.3115/1219840.1219885 http://doi.org/10.3115/1219840.1219885

Ye F , Chen W , Zhou GG , et al . Intelligent recognition of named entities in electronic medical records . Chin J of Biomed Eng 2011 ; 30(2):256-62. Chinese. doi: DOI: 10.3969/j.issn.0258-8021.2011.02.014 http://doi.org/10.3969/j.issn.0258-8021.2011.02.014 .

Li W , Zhao DZ , Li B , et al . Entity recognition of medical records combined with CRF and rules . App Res Comput 2015 ; 32 ( 4 ): 1082 - 6 . doi: DOI: 10.3969/j.issn.1001-3695.2015.04.029 http://doi.org/10.3969/j.issn.1001-3695.2015.04.029 .

Bollegala D , Matsuo Y , Ishizuka M . Relation adaptation: learning to extract novel relations with minimum supervision. Proceedings of the 22nd International Joint Conference of Artificial Intelligence (IJCAI). 2011 Jul 16-22; Barcelona, Spain . Menlo Park, California, USA : AAAI Press ; 2011 . p. 2205 - 10 . doi : doi: DOI: 10.5591/978-1-57735-516-8/IJCAI11-368 http://doi.org/10.5591/978-1-57735-516-8/IJCAI11-368 .

Suchanek FM , Ifrim G , Weikum G . Combining linguistic and statistical analysis to extract relations from web documents. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006 Aug 20-23; Philadelphia, USA . New York: ACM ; 2006 . p. 712 - 7 . doi: DOI: 10.1145/1150402.1150492 http://doi.org/10.1145/1150402.1150492 .

Qin B , Liu AA , . Unsupervised Chinese Open Entity Relation Extraction . J Comput Res Dev 2015 ; 52 ( 5 ): 1029 - 35 . Chinese. doi: DOI: 10.7544/issn1000-1239.2015.20131550 http://doi.org/10.7544/issn1000-1239.2015.20131550 .

Uzuner O , Mailoa J , Ryan R , et al . Semantic relations for problem-oriented medical records . Artif Intell Med 2010 ; 50 ( 2 ): 63 - 73 . doi: DOI: 10.1016/j.artmed.2010.05.006 http://doi.org/10.1016/j.artmed.2010.05.006 https://linkinghub.elsevier.com/retrieve/pii/S0933365710000710 https://linkinghub.elsevier.com/retrieve/pii/S0933365710000710

Zhou G , Su J , Zhang J , et al . Exploring various knowledge in relation extraction. ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics . 2005 Jun 25-30; Michigan, USA. Stroudsburg, PA, USA: Association for computational Linguistics ; 2005 . p. 427 - 34 . doi: DOI: 10.3115/1219840.1219893 http://doi.org/10.3115/1219840.1219893 .

Uzuner O , South BR , Shen S , et al . 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text . J Am Med Inform Assn 2011 ; 18 ( 5 ): 552 - 6 . doi: DOI: 10.1136/amiajnl-2011-000203 http://doi.org/10.1136/amiajnl-2011-000203 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000203 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000203

Rink B , Harabagiu S , Roberts K . Automatic extraction of relations between medical concepts in clinical texts . J Am Med Inform Assn 2011 ; 18 ( 5 ): 594 - 600 . doi: DOI: 10.1136/amiajnl-2011-000153 http://doi.org/10.1136/amiajnl-2011-000153 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000153 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000153

Demner-Fushman D , Mork JG , Shooshan SE , et al . UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text . J Biomed Inform 2010 ; 43 ( 4 ): 587 - 94 . doi: DOI: 10.1016/j.jbi.2010.02.005 http://doi.org/10.1016/j.jbi.2010.02.005 https://linkinghub.elsevier.com/retrieve/pii/S1532046410000201 https://linkinghub.elsevier.com/retrieve/pii/S1532046410000201

Lv X , Yi G , Yang J , et al . Clinical relation extraction with deep learning . Int J Inf Tech Decis 2016 ; 9 ( 7 ): 237 - 48 . doi: DOI: 10.14257/ijhit.2016.9.7.22 http://doi.org/10.14257/ijhit.2016.9.7.22 .

Pennington J , Socher R , Manning C . Glove: Global vectors for word representation . Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 2014 Oct 25-29; Doha, Qatar . p. 1532 - 43 . doi: DOI: 10.3115/v1/D14-1162 http://doi.org/10.3115/v1/D14-1162 .

Yin W , Kann K , Yu M , et al . Comparative study of CNN and RNN for natural language Processing 2017 ; arXiv: 1702 . 01923 .

Yang JF , Yu QB , Guan Y , et al . A Review of research on name recognition and entity relation extraction of electronic medical records . Acta Automatica Sinica 2014 ; 40 ( 8 ): 1537 - 62 . doi: DOI: 10.3724/SP.J.1004.2014.01537 http://doi.org/10.3724/SP.J.1004.2014.01537 .

Jiang M , Chen Y , Liu M , et al . A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries . J Am Med Inform Assn 2011 ; 18 ( 5 ): 601 - 6 . doi: DOI: 10.1136/amiajnl-2011-000163 http://doi.org/10.1136/amiajnl-2011-000163 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000163 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000163

De Bruijn B , Cherry C , Kiritchenko S , et al . Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010 . J Am Med Inform Assn 2011 ; 18 ( 5 ): 557 - 62 . doi: DOI: 10.1136/amiajnl-2011-000150 http://doi.org/10.1136/amiajnl-2011-000150 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000150 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000150

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