Healthcare Informatics Research

  • 제24권4호
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  • Pages.376-380
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  • 2018
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  • 2093-3681(pISSN)
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  • 2093-369X(eISSN)
대한의료정보학회 (Korean Society of Medical Infomatics)
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DOI QR코드

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Design and Construction of a NLP Based Knowledge Extraction Methodology in the Medical Domain Applied to Clinical Information

  • 투고 : 2018.07.25
  • 심사 : 2018.10.26
  • 발행 : 2018.10.31
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: This research presents the design and development of a software architecture using natural language processing tools and the use of an ontology of knowledge as a knowledge base. Methods: The software extracts, manages and represents the knowledge of a text in natural language. A corpus of more than 200 medical domain documents from the general medicine and palliative care areas was validated, demonstrating relevant knowledge elements for physicians. Results: Indicators for precision, recall and F-measure were applied. An ontology was created called the knowledge elements of the medical domain to manipulate patient information, which can be read or accessed from any other software platform. Conclusions: The developed software architecture extracts the medical knowledge of the clinical histories of patients from two different corpora. The architecture was validated using the metrics of information extraction systems.

키워드

과제정보

연구 과제 주관 기관 : National Secretariat of Science and Technology of Panama (SENACYT)

참고문헌

  1. Blaya JA, Fraser HS, Holt B. E-health technologies show promise in developing countries. Health Aff (Millwood) 2010;29(2):244-51. https://doi.org/10.1377/hlthaff.2009.0894
  2. Terzieva M. Project knowledge management: how organizations learn from experience. Procedia Technol 2004;16:1086-95.
  3. Maedche A, Motik B, Stojanovic L, Studer R, Volz R. Ontologies for enterprise knowledge management. IEEE Intell Syst 2003;18(2):26-33. https://doi.org/10.1109/MIS.2003.1193654
  4. Studer R, Benjamins VR, Fensel D. Knowledge engineering: principles and methods. Data Knowl Eng 1998;25(1):161-98. https://doi.org/10.1016/S0169-023X(97)00056-6
  5. Legaz-Garcia Mdel C, Menarguez-Tortosa M, Fernandez-Breis JT, Chute CG, Tao C. Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes. J Am Med Inform Assoc 2015;22(3):536-44.
  6. Friedman C, Rindflesch TC, Corn M. Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. J Biomed Inform 2013;46(5):765-73. https://doi.org/10.1016/j.jbi.2013.06.004
  7. Ruiz-Martinez JM, Valencia-Garcia R, Fernandez-Breis JT, Garcia-Sanchez F, Martinez-Bejar R. Ontology learning from biomedical natural language documents using UMLS. Expert Syst Appl 2011;38(10):12365-78. https://doi.org/10.1016/j.eswa.2011.04.016
  8. Inniss TR, Lee JR, Light M, Grassi MA, Thomas G, Williams AB. Towards applying text mining and natural language processing for biomedical ontology acquisition. Proceedings of the 1st International Workshop on Text Mining in Bioinformatics; 2006 Nov 10; Arlington, VA. p. 7-14.
  9. Manine AP, Alphonse E, Bessieres P. Information extraction as an ontology population task and its application to genic interactions. Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence; 2008 Nov 3-5; Dayton, OH. p. 74-81.
  10. Quesada-Martinez M, Mikroyannidi E, Fernandez-Breis JT, Stevens R. Approaching the axiomatic enrichment of the Gene Ontology from a lexical perspective. Artif Intell Med 2015;65(1):35-48. https://doi.org/10.1016/j.artmed.2014.09.003
  11. Thakker D, Osman T, Lakin P. GATE JAPE grammar tutorial [Internet]. Sheffield: The University of Sheffield; 2009 [cited at 2018 Oct 1]. Available from: https://gate.ac.uk/sale/thakker-jape-tutorial/GATE%20JAPE%20manual.pdf.
  12. IJntema W, Sangers J, Hogenboom F, Frasincar F. A lexico-semantic pattern language for learning ontology instances from text. Web Semant 2012;15:37-50. https://doi.org/10.1016/j.websem.2012.01.002
  13. Wyner AZ, Schneider J, Atkinson K, Bench-Capon TJ. Semi-automated argumentative analysis of online product reviews. COMMA 2012;245:43-50.
  14. Lee CH, Wang SH. (2012). An information fusion approach to integrate image annotation and text mining methods for geographic knowledge discovery. Expert Syst Appl 2012;39(10):8954-67. https://doi.org/10.1016/j.eswa.2012.02.028
  15. Horridge M, Knublauch H, Rector A, Stevens R, Wroe C. A practical guide to building OWL ontologies using the Protege-OWL plugin and CO-ODE tools, Edition 1.0 [Internet]. Manchester: The University of Manchester; 2014 [cited at 2018 Oct 1]. Available from: http://mowlpower.cs.man.ac.uk/protegeowltutorial/resources/ProtegeOWLTutorialP3_v1_0.pdf
  16. Fernandez-Breis JT, Chiba H, Legaz-Garcia Mdel C, Uchiyama I. The orthology ontology: development and applications. J Biomed Semantics 2016;7(1):34. https://doi.org/10.1186/s13326-016-0077-x
  17. Lupse O, Vida M, Stoicu-Tivadar L, Stoicu-Tivadar V. Using HL7 CDA and CCD standards to improve communication between healthcare information systems. Proceedings of 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY); 2011 Sep 8-10; Subotica, Serbia. p. 453-7.

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