Determining the syntactic structure of medical terms in clinical notes

Authors:
Bridget T. McInnes;Ted Pedersen;Serguei V. Pakhomov
Affiliations:
University of Minnesota, Minneapolis, MN;University of Minnesota Duluth, Duluth, MN;University of Minnesota, Minneapolis, MN
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

This paper demonstrates a method for determining the syntactic structure of medical terms. We use a model-fitting method based on the Log Likelihood Ratio to classify three-word medical terms as right or left-branching. We validate this method by computing the agreement between the classification produced by the method and manually annotated classifications. The results show an agreement of 75%--83%. This method may be used effectively to enable a wide range of applications that depend on the semantic interpretation of medical terms including automatic mapping of terms to standardized vocabularies and induction of terminologies from unstructured medical text.