Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Franz, P; Zaiss, A; Schulz, S; Hahn, U; Klar, R.
Automated coding of diagnoses - Three methods compared
J AMER MED INFORM ASSOC. 2000; 250-254.
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- Co-Autor*innen der Med Uni Graz
- Schulz Stefan
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- Abstract:
- In Germany, new legal requirements have raised the importance of the accurate encoding of admission and discharge diseases for in- and outpatients. In response to emerging needs for computer-supported tools we examined three methods for automated coding of German-language free-text diagnosis phrases. We compared a language-independent lexicon-free n-gram approach with one which uses a dictionary of medical morphemes and refines the query by a mapping to SNOMED codes. Both techniques produced a ranked output of possible diagnoses within a vector space framework for retrieval. The results did not reveal any significant difference: The correct diagnosis was found in approximately 40% for three-digit codes, and 30% for four-digit codes. The lexicon-based method was then modified by substituting the vector space ranking by a heuristic approach that capitalizes on the semantic structure of SNOMED, thus raising the number of correct diagnoses significantly (approximately 50% for three-digit codes, and 40% for four-digit codes). As a result, we claim that lexicon-based retrieval methods do not perform better than the lexicon-free ones, unless conceptual knowledge is added.
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