Abstract
One of the most critical problems of automatic natural language processing (NLP) is the size of the medical dictionaries. The set of compound medical words and the often used possibility to create new terms render the exhaustivity of medical dictionaries beyond question. The structure of such dictionaries is usually composed of two parts: the first one generally contains morphological and sometimes syntactical information necessary to identify, on a grapheme level, a given word in a sentence whereas the second part is often devoted to conceptual knowledge associated with the recognised word. It is only when these two prerequisites are fulfilled that an attempt to understand the meaning of a whole expression is possible. The approach developed in this paper shows the pragmatic method used to implement a powerful analyser dedicated to help physicians or coding clerks to encode medico-economic information about patients using international classifications like ICD. It describes how to build medical dictionaries that can help the application of morphological and conceptual analysers (encoders). The methods used have proved to be efficient for various classifications as well as for multiple languages as the system presently supports French, German, English and Dutch for the full ICD-10 classification.
Preview
Unable to display preview. Download preview PDF.
References
Chute CG, Atkin GE, Ihrke DM. An empirical evaluation of concept capture by clinical classifications. In: Proceedings MEDINFO 92 (Ed. Lun KC, Degoulet P, Piemme TE, Rienhoff O), North-Holland, Amsterdam, 1992, pp.1469–1474
Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB. Discordance of databases designed for claims payment versus clinical information systems. Ann Intern Med, 119:844–850, 1993
Lovis C, Michel PA, Borst F, Baud R, Griesser V, Scherrer JR Medico-Economic Patient Encoding in the University Hospital of Geneva. Proceedings
K. Koskenniemi. Two-level model for morphological analysis. PhD Thesis. University of Helsinki, 1983
K. Matsuno. Semantic commitments as a mode of non-programmable computation in the brain. Biosystems (Netherlands), 27/4: 235–239, 1992
LJJ. Wittgenstein. Philosophical Investigations. Oxford: Basil Blackwell, 1953
F. de Saussure (1915). Cours de linguistique générale. Bally & Sechehaye, Ed. Payot, 1966
MG. Pacak, LM. Norton, GS. Dunham. Morphosemantic Analysis of-ITIS Forms in Medical Language. Meth Inform Med, 19: 99–105, 1980
LM. Norton, MG. Pacak. Morphosemantic Analysis of Compoud Word Forms Denoting Surgical Procedures. Meth Inform Med, 22: 29–36, 1983
S. Wolff. The Use of Morphosemantic Regularities in the Medical Vocabulary for Automatic Lexical Coding. Meth Inform Med, 23: 195–203, 1984
P. Dujols, P. Aubas, C. Baylon, F. Grémy. Morphosemantic Analysis and Translation of Medical Compound Terms. Meth Inform Med, 30: 30–35, 1991
Brigl B., Mieth M., Haux R., Glück W., The LBI-method for automated indexing of diagnoses by using SNOMED. Part 1. Int J Bio Med Computing, 37: 237–247, 1994
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lovis, C., Baud, R., Michel, P.A., Scherrer, J.R., Rassinoux, A.M. (1997). Building medical dictionaries for patient encoding systems: A methodology. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029470
Download citation
DOI: https://doi.org/10.1007/BFb0029470
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62709-8
Online ISBN: 978-3-540-68448-0
eBook Packages: Springer Book Archive