A General Natural-language Text Processor for Clinical Radiology
- Affiliation of the authors: Columbia University (CF,JJC, SBJ) and Columbia Presbyterian Medical Center (POA,JHMA), New York, NY
- Correspondence and Reprints: Carol Friedman, PhD, Queens College of the City University of New York, Computer Science Department, Flushing, NY 11367-1597.
Abstract
Objective Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms.
Design The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi- word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain.
Measurements The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision.
Results Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
Footnotes
-
Supported in part by Grant Number R29 LM05397 form the National Library of Medicine and Grant Number 6-61483 from the Research Foundation of CUNY.
