Identifying Relations between Medical Concepts by Parsing UMLS??Definitions

详细信息    查看全文

• 作者：Ivelina Nikolova (1) iva@lml.bas.bg
  Galia Angelova (1) galia@lml.bas.bg
• 关键词：relation extraction – clinical terms – biomedical NLP
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：6828
• 期：1
• 页码：173-186
• 全文大小：283.4 KB
• 参考文献：1. Terminological Services of Unified Medical Language System (UMLS), https://uts.nlm.nih.gov/home.html
  2. Project Effective search of conceptual information with applications in medical informatics, http://www.lml.bas.bg/evtima
  3. Sowa, J.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)
  4. Nirenburg, S., McShane, M., Zabludowski, M., Beale, S., Pfeifer, C.: Ontological Semantic Text Processing in the Biomedical Domain. University of Maryland Baltimore County, Institute for Language and Information Technologies, Working Paper, 3–5, http://naboo.ilit.umbc.edu/ILIT_Working_Papers/ILIT_WP_03-05_Biomed_Mesh.pdf (last visited February 2011)
  5. Buitelaar, P., Olejnik, D., Sintek, M.: A Protg Plug-In for Ontology Extraction from Text Based on Linguistic Analysis. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 31–44. Springer, Heidelberg (2004)
  6. Schutz, A., Buitelaar, P.: RelExt: A Tool for Relation Extraction from Text in Ontology Extension. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A., et al. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 593–606. Springer, Heidelberg (2005)
  7. RelEx, https://launchpad.net/relex
  8. Fundel, K., Kffner, R., Zimmer, R.: RelEx - Relation extraction using dependency parse trees. Journal of Bioinformatics 23(3), 365–371 (2007)
  9. Denecke, K.: Enchancing Knowledge Representations by Ontological Relations. In: Andersen, K., et al. (eds.) Proc. of MIE 2008, eHealth Beyond the Horizon - Get IT There, Goteborg. Studies in Health Technology and Informatics, vol. 136, pp. 791–796. IOS Press, Amsterdam (2008)
  10. Vintar, S., Buitelaar, P., Volk, M.: Semantic Relations in Concept-based Cross-language Medical Information Retrieval. In: Adaptive Text Extraction and Mining (ATEM), Cavtat-Dubrovnik (2003)
  11. Vintar, S., Todorovski, L., Sonntag, D., Buitelaar, P.: Evaluating context features for medical relation mining. In: ECML/PKDD Workshop on Data Mining and Text Mining for Bioinformatics (2003)
  12. Chapman, W., Cohen, K.B.: Current issues in biomedical text mining and natural language processing. Journal of Biomedical Informatics 42(5), 757–759 (2009)
  13. Diabetes Ontology at the BioPortal provided by the Medical Dictionary for Regulatory Activities Terminology (MedDRA), http://bioportal.bioontology.org/visualize/42280/?conceptid=10012614 (last visted February 2011)
  14. Browne, A.C., Guy, D., Aronson, A., McCray, A.: UMLS language and vocabulary tools. In: Proceedings Annual Symposium AMIA p. 798 (2003)
  15. Link Grammar Parser, http://www.link.cs.cmu.edu/link/
  16. Current Relations in the UMLS Semantic Network, http://www.nlm.nih.gov/research/umls/META3_current_relations.html
  17. Terminological Services of Unified Medical Language System (UMLS), Developer’s Guide, https://uts.nlm.nih.gov//doc/devGuide/index.html
  18. Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41 (1995)
  19. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
• 作者单位：1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 25A, Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

To automatically analyse medical narratives, one needs linguistic and conceptual resources which support capturing of important information from texts and its representation in a structured way. Thus the conceptual structures encoding domain concepts and relations are crucial for the development of reliable and high-performance information extraction system. We present research work enabling automatic extraction of relations between medical concepts. The lack of conceptual resources with Bulgarian ontological vocabulary provoked us to reuse already existing resources with English labels, more especially the UMLS? Metathesaurus?. We form a terminological dictionary of the Bulgarian terms of interest, translate them to English and extract their UMLS definitions which are short English statements in free text. These definitions are processed automatically by a semantic parser; afterwards we apply additional extraction, alternation and validation rules and built a set of new relations to be inserted in our conceptual resource. The article presents the input data and available tools, the knowledge chunks extracted from UMLS and their processing, as well as a discussion of the present results.