Artificial Intelligence in Medicine
Volume 39, Issue 2 , Pages 137-149 , February 2007

Extraction and use of linguistic patterns for modelling medical guidelines

  • Radu Serban

      Affiliations

    • Artificial Intelligence Department, Vrije Universiteit, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands
    • Corresponding Author InformationCorresponding author. Tel.: +31 20 598 7818; fax: +31 20 598 7653.
    • ,
  • Annette ten Teije

      Affiliations

    • Artificial Intelligence Department, Vrije Universiteit, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands
    • ,
  • Frank van Harmelen

      Affiliations

    • Artificial Intelligence Department, Vrije Universiteit, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands
    • ,
  • Mar Marcos

      Affiliations

    • Department of Computer Engineering and Science, Universitat Jaume I, Castellon, Spain
    • ,
  • Cristina Polo-Conde

      Affiliations

    • Department of Computer Engineering and Science, Universitat Jaume I, Castellon, Spain

Received 16 January 2006 ,Revised 26 July 2006 ,Accepted 28 July 2006.

References 

  1. Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform. 2003;36(6):462–477
  2. Protocure 2 Project. Integrating formal methods in the development process of medical guidelines and protocols. URL: http://www.protocure.org. Accessed: 1 June 2006.
  3. Svatek V, Ruzicka M. Mark-up based analysis of narrative guidelines with the Stepper tool. In:  Kaiser K,  Miksch S,  Tu SW editor. Proceedings of Symposium on Computerized Guidelines and Protocols (CGP-04). Prague, Czech Republic: IOS Press; 2004;p. 132–136
  4. Moser M, Miksch S. Improving clinical guideline implementation through prototypical design patterns. In:  Keravnou E,  Miksch S,  Hunter J editor. Proceedings of 10th Conference on Artificial Intelligence in Medicine (AIME 2005). Aberdeen, UK: Springer-Verlag GmbH; 2005 July;p. 126–130[LNCS 3581]
  5. Uren V, Buckingham S, Mancini C, Li G. Modelling naturalistic argumentation in research literatures. In: Proceedings of 4th Workshop on Computational Models of Natural Argument, Valencia, Spain, 22–27 August; 2004.
  6. Ruch P, Rassinoux AM, Baud RH, Lovis C. A light knowledge model for linguistic applications. In: Proceedings of American Medical Informatics Association Symposium. Medical Informatics Division, University Hospital of Geneva, Switzerland. 2001;p. 37–41
  7. Aronson AR. Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. In: Proceedings of American Medical Informatics Association Symposium. National Library of Medicine; 2001;p. 17–21
  8. Rindflesch TC, Aronson AR. Semantic processing for enhanced access to biomedical knowledge. In:  Kashyap V,  Shklar L editor. Real World Semantic Web Applications. IOS Press; 2002;p. 157–172
  9. Hahn U, Schulz S, Romacker M. How knowledge drives understanding–matching medical ontologies with the needs of medical language processing. Int J Artif Intell Med. 1999;15(1):25–51
  10. Balser M, Coltell O, van Croonenborg J, Duelli C, van Harmelen F, Jovell A,, et al. Protocure: supporting the development of medical protocols through formal methods. In:  Kaiser K,  Miksch S,  Tu SW editor. Proceedings of Symposium on Computerized Guidelines and Protocols (CGP-04). Prague, Czech Republic: IOS Press; 2004 April;
  11. Bury J, Ciccarese P, Fox J, Greenes RA, Hall R, Johnson PD,, et al. Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc. 2003;10(1):52–68
  12. Seyfang A, Kosara R, Miksch S, Votruba P. Tools for acquiring clinical guidelines in ASBRU. In: Proceedings of the 6th World Conference on Integrated Design and Process Technology (IDPT’02). 2002;
  13. Seyfang A, Miksch S, Marcos M, Wittenberg J, Rosenbrand K, Polo-Conde C. Bridging the gap between informal and formal guideline representations. In: Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006). Riva del Garda, Italy. 2006;
  14. Beckworth R, Fellbaum C, Gross D, Miller G. WordNet: A lexical database organized on psycholinguistic principles. In:  Zernik U editors. Lexical Acquisition: Exploiting On-line Resources to Build a Lexicon. Lawrence Erlbaum Associates; 1991;p. 211–226
  15. Spackman KA, Campbell KE, Cote RA. SNOMED RT: A reference terminology for health care. J Am Med Inform Assoc; 1997. p. 640–4 [Fall Symposium Supplement].
  16. Rector A, Gangemi A, Galeazzi E, Glowinski A, Rossi-Mori A. The GALEN CORE model schemata for anatomy: towards a re-usable application-independent model of medical concepts. In: Proceedings of the Twelfth International Congress of the European Federation for Medical Informatics in Europe (MIE 94). Lisbon, Portugal. 1994;p. 229–233
  17. Mesh (Medical Subject Headings (MESH)). URL: http://www.nlm.nih.gov/mesh/meshhome.html. Accessed: 1 June 2006.
  18. Unified Medical Language System (UMLS). URL: http://www.nlm.nih.gov/research/umls/. Accessed: 1 June 2006.
  19. National Cancer Institute (NCI) ontology. URL: http://www.mindswap.org/2003/CancerOntology/. Accessed: 1 June 2006.
  20. Johnson S. A semantic lexicon for medical language processing. J Am Med Inform Assoc. 1999;6(3):205–218
  21. Dutch Institute for Healthcare Quality (CBO), Guideline for the Treatment of Breast Carcinoma; 2002. PMID: 12474555.
  22. Seyfang A, Miksch S, Polo-Conde C, Wittenberg J, Marcos M, Rosenbrand K. MHB—a many-headed bridge between informal and formal guideline representations. In:  Miksch S,  Hunter J,  Keravnou E editor. Proceedings of 10th Conference on Artificial Intelligence in Medicine (AIME 2005). Springer; 2005;p. 146–150
  23. Tu SW, Musen MA. A Flexible Approach to Guideline Modeling. In:  Nancy Lorenzi P editors. 1999 AMIA Annual Fall Symposium. Washinton D.C.: Hanley & Belfus Inc.; 1999;p. 475–497
  24. Shahar Y, Young O, Shalom E, Mayaffit A, Moskovitch R, Hessing A,, et al. DeGeL: A Hybrid, Multiple-Ontology Framework for Specification and Retrieval of Clinical Guidelines. In:  Dojat M,  Keravnou ET,  Barahona P editor. Proceedings of 9th European Conference on AI in Medicine (AIME 03). Protaras, Cyprus, October 18–22, 2003. LNCS 2780. Heidelberg: Springer-Verlag; 2003;p. 122–131
  25. Ciccarese P, Caffi E, Boiocchi L, Alevi H, Quaglini S, Kumar A, et al. The NewGuide Project: guidelines, information sharing and learning from exceptions. In:  Dojat M,  Keravnou ET,  Barahona P editor. Proceedings of 9th European Conference on AI in Medicine (AIME 03). Protaras, Cyprus, October 18–22, 2003. LNCS 2780. Heidelberg: Springer-Verlag; 2003;
  26. Friedman C, Hripcsak G. Evaluating natural language processors in the clinical domain. In: CC Chute, editor. Proceeding Conference on Natural Language and Medical Concept Representation; 1997. p. 41–52, http://citeseer.ist.psu.edu/friedman97evaluating.html.
  27. Hahn U, Romacker M, Schulz S. medSyndikate: a natural language system for the extraction of medical information from findings reports. Int J Med Inform. 2000;67(1/3):63–74
  28. Paynter GW, Witten IH, Gutwin C, Frank E, Nevill-Manning C. Domain-specific keyphrase extraction. In: Proceedings of the International Joint Conference on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers; 1999;p. 668–673
  29. Cimiano P, Volker J. Text2Onto: A framework for ontology learning and data-driven change discovery. In:  Metais E,  Montoyo A,  Munoz R editor. Natural Language Processing and Information Systems: 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005. vol. 3513, Alicante, Spain, June 15–17, 2005. Springer; 2005;p. 227–238[LNCS]
  30. Moreno A, Perez C. From text to ontology: extraction and representation of conceptual information. In: Proceedings of the 4th Conference Terminology and Artificial Intelligence, Nancy, France; 3–4 May 2001.
  31. Riloff E. Automatically generating extraction patterns from untagged text. In: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96). 1996;p. 1044–1049
  32. Riloff E, Shoen J. Automatically acquiring conceptual patterns without an annotated corpus. In: Yarovsky D, Church K, editors, Proceedings of the Third Workshop on Very Large Corpora. Somerset, New Jersey: Association for Computational Linguistics; 1995. p. 148–61.
  33. Huffman SB. Learning information extraction patterns from examples. In:  Wermter S,  Riloff E,  Scheler G editor. Connectionist, statistical, and symbolic approaches to learning for natural language processing. Berlin: Springer; 1995;p. 246–260
  34. Witten I. Adaptive text mining: inferring structure from sequences. J Discrete Algorithms. 2004;2(2):137–159
  35. Zhong J, Zhu H, Li J, Yu Y. Conceptual graph matching for semantic search. In: Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces. Springer-Verlag; 2002;p. 92–106
  36. Culotta A, McCallum A, Betz J. Integrating probabilistic extraction models and data mining to discover relations and patterns in text. In: Proceedings of the Human Language Technology Conference, HLT-NAACL 2006. New York City: Association for Computational Linguistics; 2006 June;
  37. Ceusters W, Buekens F, de Moor G, Waagmeester A. The distinction between linguistic and conceptual semantics in medical terminology and its implication for NLP-based knowledge acquisition. Methods Inform Med. 1998;37(4–5):327–333
  38. do Amaral MB, Roberts A, Rector AL. NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs. In:  Overhage JM editors. Proceedings of the 2000 American Medical Informatics Association Annual Symposium (AMIA 2000). Los Angeles: American Medical Informatics Association; 2000;p. 76–80
  39. Leroy G, Chen H. GeneScene: An ontology-enhanced integration of linguistic and co-occurrence based relations in biomedical texts. J Am Soc Inform Sci Technol. 2005;56(5):457–468
  40. Deneke K, Kohlhof I, Bernauer J. Use of multiaxial indexing for information extraction from medical texts. In: Proceedings of the Workshop on Foundations of Clinical Terminology and Classifications (FCTC 06), Timisoara, Romania. ROMEDINF; April 2006.
  41. Ciravegna F. Adaptive Information Extraction from Text by Rule Induction and Generalization. In:  Nebel B editors. Proceedings of the Seventeenth International Conference on Artificial Intelligence (IJCAI-01). San Francisco, CA: Morgan Kaufmann Publishers, Inc.; 2001;p. 1251–1256

☆ This is an extended and revised version of our paper presented in the Conference on Artificial Intelligence in Medicine (AIME 05).

PII: S0933-3657(06)00113-8

doi: 10.1016/j.artmed.2006.07.012

Artificial Intelligence in Medicine
Volume 39, Issue 2 , Pages 137-149 , February 2007

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