Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports

Abstract

Identification of eligible patients from electronically available patient data is a key difficulty in computerizing clinical practice guidelines because a large amount of the relevant data is stored as free text. We have been using MedLEE (Medical Language Extraction and Encoding System), a natural language processing system, to encode the clinical information in all chest radiograph and mammogram reports. This paper describes a retrospective study to determine if MedLEE can identify patients at risk for having tuberculosis (TB) based on their admission chest radiographs. Reports of 171 adult inpatients with culture-positive TB during 1992 and 1993 were manually coded (by a TB specialist) using seven terms suggestive of TB, and were also encoded by MedLEE. Using manual coding as the gold standard, MedLEE agreed on the classification of 152/171 (88.9%) reports--129/142 (90.8%) suspicious for TB and 23/29 (79.3%) not suspicious for TB; and 1072/1197 (89.6%) terms indicative of TB. Analysis showed that most of the discrepancies were caused by MedLEE not finding the location of the infiltrate. By ignoring the location of the infiltrate, the agreement became 157/171 (91.8%) reports and 946/1026 (92.2%) terms. Thus, natural language processing offers a practical alternative for using free-text reports to determine patient eligibility for computerized clinical practice guidelines.