A natural language processing system to extract and code concepts relating to congestive heart failure from chest radiology reports

Abstract

We have developed a natural language processing system for extracting and coding clinical data from free text reports. The system is designed to be easily modified and adapted to a variety of free text clinical reports such as admission notes, radiology and pathology reports, and discharge summaries. This report presents the results of this system to extract and code clinical concepts related to congestive heart failure from 39,000 chest radiology reports. The system detects the presence or absence of six concepts: congestive heart failure, Kerley B lines, cardiomegaly, prominent pulmonary vasculature, pulmonary edema, and pleural effusion. We compared it's output to a gold standard which consisted of specially trained human coders as well as an experienced physician. Results indicate that the system had high specificity, recall and precision for each of the concepts it is designed to detect.

Figure 1

Classic findings of CHF on a chest x-ray including cardiomegaly, pulmonary edema, pleural effusions and prominent vasculature.

Figure 2

Processing Schema of REX

Figure 3

Example of REX incorrectly coding report as CHF positive

Figure 4

Example of report where CHF not coded by human coders but found by REX