Empirical, automated vocabulary discovery using large text corpora and advanced natural language processing tools.
- 1 January 1996
- journal article
- p. 159-63
Abstract
A major impediment to the full benefit of electronic medical records is the lack of a comprehensive clinical vocabulary. Most existing vocabularies do not allow the full expressiveness of clinical diagnoses and findings that are often qualified by modifiers relating to severity, acuity, and temporal factors. One reason for the lack of expressivity is the inability of traditional manual construction techniques to identify the diversity of language used by clinicians. This study used advanced natural language processing tools to identify terminology in a clinical findings domain, compare its coverage with the UMLS Metathesaurus, and quantify the effort required to discover the additional terminology. It was found that substantial amounts of phrases and individual modifiers were not present in the UMLS Metathesaurus and that modest effort in human time and computer processing were needed to obtain the larger quantity of terms.This publication has 9 references indexed in Scilit:
- The Canon Group's Effort: Working Toward a Merged ModelJournal of the American Medical Informatics Association, 1995
- Toward a Medical-concept Representation LanguageJournal of the American Medical Informatics Association, 1994
- The Position of the Canon Group: A Reality CheckJournal of the American Medical Informatics Association, 1994
- A General Natural-language Text Processor for Clinical RadiologyJournal of the American Medical Informatics Association, 1994
- Computerized physician order entry and quality of care.1994
- Knowledge-based Approaches to the Maintenance of a Large Controlled Medical TerminologyJournal of the American Medical Informatics Association, 1994
- Toward Representations for Medical ConceptsMedical Decision Making, 1991
- Automatic Indexing of Abstracts via Natural-language Processing Using a Simple Thesaurus:Medical Decision Making, 1991
- Reminders to Physicians from an Introspective Computer Medical RecordAnnals of Internal Medicine, 1984