Medical Languages: Use, Definition and Processing in Ward Information Systems (WIS)

  • J.-R. Scherrer
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 45)


It is quite commonplace to observe nowadays how medical records are reduced to a huge mass of poorly structured information, that is to say a mixture of texts, images and numbers. Within medical institutions, particularly hospitals, the situation becomes worse when there is the least attempt to retrieve specific, relevant information, let us say, first patient-to-patient and then by medical entities or on an institutional statistical basis. It is not always a surprise why the bibliographical research is so paramount, since it is generally accepted that the easiest accessible relevant information is from medical literature thanks to powerful tools like MEDLINE, Excerpta Medica or Pergamon.


Medical Informatics Discharge Letter Proximity Processing Natural Language Interface Bibliographical Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    D.A.B. Lindberg, B.L. Humphreys: “The UMLS Knowledge Sources: Tools for Building Better User Interfaces” In: Proceedings of the 14th Annual Symposium in Medical Care, R.A. Miller (Ed.), New York: IEEE Comp. Soc. Press 1990: 121–125.Google Scholar
  2. [2]
    F.R. Borst, J.-C. Chevrolet, P.-F. Unger, J.-R. Scherrer: “How to Promote High Level Medical Standards of Care in a Teaching Hospital”; In: Medical Informatics Europe !88, R. Hansen, B.G. Solheim, R.R. OfMoore, F.H. Roger (Eds.): Springer-Verlag, Berlin 1988: pp. 133–136.Google Scholar
  3. [3]
    C. Safran, D. Porter, J. Lightfoot, C.D. Rury, L.H. Underhill, H.L. Bleich, W.V. Slack: “ClinQuery: A System for Online Searching of Data in a Teaching Hospital”: Ann. Int. Med. 1989: 113 (9): 751–756.Google Scholar
  4. [4]
    J.G. Mazone: “Diagnosis without Doctors”: Journal of Medicine and Philosophy: 1990.Google Scholar
  5. [5]
    S.B. Flexma, J.Stein, (Editors): 1988 “The Random House College Dictionary”: Revised Edition, Random House, Inc. New York: pp. 366.Google Scholar
  6. [6]
    R.A. Miller: “Why The Standard View is Standard: People, Not Machines, Understand Patients1 Problems”: 1991 (to be published).Google Scholar
  7. [7]
    R.A. Miller and F.E. Masarie Jr.: “The Demise of the Greek Oracle Model for Medical Diagnostic Systems”: Meth. Inform. Med. 29 (1990): 1–2.Google Scholar
  8. [8]
    “QMR User Manual-Version 1.0 for the DOS Operating System”: University of Pittsburgh, Camdat Corp. 1990: pp. 161–171.Google Scholar
  9. [9]
    R.A. Miller, F.E. Masarie,Jr.: “Quick Medical Reference (QMR)- An Evolving, Microcomputer-based Diagnostic Decision-Support Program For General Internal Medicine” In: Proceedings of the 13th Annual Symposium in Medical Care. New York: IEEE Comp. Soc. Press 1989: 947–8.Google Scholar
  10. [10]
    A.W. Pratt: “Medicine, Computers and Linguistics”, Adv. Biomed. Eng. Academic Press, New York, 1973: 3-97-140.Google Scholar
  11. [11]
    M. King: “Are There Any Lessons to be Learned from Machine Translation?” In: Computerised Natural Medical Language Processing for Knowledge Representations: J.-R. Scherrer, R.A. Côté, S.H. Mandil (Eds.): Elsevier Science Publishers, B.V. (North-Holland): IMIA (1989): pp. 73–82.Google Scholar
  12. [12]
    C.R. Perrault, B. J. Grosz: “Natural-Language Interfaces”: Ann. Rev. Comp. Sc., 1986, 1: 47–82.CrossRefGoogle Scholar
  13. [13]
    J. Davidson, S.J. Kaplen: “Natural Language Access to Databases Interpreting update requests”: Am. J. Comp. Linguist. 9(2): 57–68.Google Scholar
  14. [14]
    N. Sager, C. Friedman, M.S. Lyman: “Medical Language Processing: Computer Management of Narrative Data”: Addison- Wesley, Reading 1987.Google Scholar
  15. [15]
    N. Sager, M. Lyman, L.J. Tick, F. Borst, Ngo Thanh Nhan, C. Revillard, Yu Su, J.-R. Scherrer: “Adapting & Medical Language Processor From English to French” In: MEDINFO 89 Proceedings, B. Barber, D. Cao, D. Qin, G. Wagner (Eds.), North-Holland, Amsterdam 1989: pp. 795–799.Google Scholar
  16. [16]
    Fourteenth Annual Symposium on Computer Applications In Medical Care, American Medical Informatics Association, IEEE Comp. Sor Press, Washington 1990: R.A. Miller (Ed.); Sections 4A–4B; pp. 121–179.Google Scholar
  17. [17]
    J. Weizenbaum: “ELIZA”, Communication of the ACM, (1966) 9: 36.CrossRefGoogle Scholar
  18. [18]
    T. Winograd: “Understanding Natural Languages”, New York Academic Press (1972).Google Scholar
  19. [19]
    D.H.D. Warren, F.C.N. Pereira: “An Efficient Easily Adaptable System for Interpreting Natural Language Queries” Am. J. Comput. Linguist. 8(3–4): 110–22.Google Scholar
  20. [20]
    S.M. Shieber: “Evidence Against the Context-Freeness of Natural Language” Linguist. Philos. 8: 333–43.Google Scholar
  21. [21]
    J.F. Sowa: “Conceptual Structures: Information Processing in Mind and Machine”: Addison-Wesley Pub. Co., New York 1984.zbMATHGoogle Scholar
  22. [22]
    A.M. Morel, R.H. Baud, J.-R. Scherrer: “Proximity Processing of Medical Text” In: Medical Informatics Europe 90, R.O’Moore, S. Bengtsson, J.R. Bryant, J.S. Bryden (Eds.), Springer-Verlag Heidelberg 1990 pp. 625–620.Google Scholar
  23. [23]
    R.H. Baud, A.-M. Rassinoux, J.-R. Scherrer: “Knowledge Representation of Discharge Summaries”: AIME (Artificial Intelligence in Medicine, Europe - accepted for presentation) 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • J.-R. Scherrer
    • 1
  1. 1.Hospital Information CenterCantonal University Hospital of GenevaGeneva 4Switzerland

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