Abstract
We discuss the development and evaluation of an automated procedure for extracting drug-dosage information from clinical narratives. The process was developed rapidly using existing technology and resources, including categories of terms from UMLS96. Evaluations over a large training and smaller test set of medical records demonstrate an approximately 80% rate of exact and partial matches' on target phrases, with few false positives and a modest rate of false negatives. The results suggest a strategy for automating general concept identification in electronic medical records.
Full text
PDF
Selected References
These references are in PubMed. This may not be the complete list of references from this article.
- Friedman C., Alderson P. O., Austin J. H., Cimino J. J., Johnson S. B. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc. 1994 Mar-Apr;1(2):161–174. doi: 10.1136/jamia.1994.95236146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haug P. J., Ranum D. L., Frederick P. R. Computerized extraction of coded findings from free-text radiologic reports. Work in progress. Radiology. 1990 Feb;174(2):543–548. doi: 10.1148/radiology.174.2.2404321. [DOI] [PubMed] [Google Scholar]