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Application of Recently Developed Computer Algorithm for Automatic Classification of Unstructured Radiology Reports: Validation Study¹

¹ From the Division of Computing and Information Services, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 100 Charles River Plaza, Suite 471, Cambridge St, Boston, MA 02114. Received January 9, 2004; revision requested March 12; revision received April 6; accepted May 19. Address correspondence to K.J.D. (e-mail: kdreyer@partners.org).

PURPOSE: To validate the accuracy of Lexicon Mediated Entropy Reduction (LEXIMER), a new information theory–based computer algorithm developed by the authors for independent analysis and classification of unstructured radiology reports based on the presence of clinically important findings (F_T, where _T represents "true") and recommendations for subsequent action (R_T).

MATERIALS AND METHODS: The study was approved by the Human Research Committee of the institutional review board. Consecutive de-identified radiology reports (n = 1059) comprising results of barium studies (n = 99), computed tomography (n = 107), mammography (n = 90), magnetic resonance imaging (n = 108), nuclear medicine (n = 99), positron emission tomography (n = 106), radiography (n = 212), ultrasonography (n = 131), and vascular procedures (n = 107) were independently analyzed by two radiologists and then with LEXIMER to categorize the reports into F_T and F_T0 (containing or not containing clinically important findings) categories and R_T and R_T0 (containing or not containing recommendations for subsequent action) categories. Accuracy, sensitivity, specificity, and positive and negative predictive values of LEXIMER for placing reports into F_T and F_T0 and R_T and R_T0 categories were assessed by using appropriate statistical tests.

RESULTS: There was strong interobserver concordance between the two radiologists for placing radiology reports into F_T and R_T categories ( = 0.9, P < .01). For the LEXIMER program, accuracy, sensitivity, specificity, and positive and negative predictive values, respectively, were 97.5% (95% confidence interval [CI]: 96.6%, 98.5%), 98.9% (95% CI: 97.9%, 99.6%), 94.9% (95% CI: 93.1%, 96.0%), 97.5% (95% CI: 96.6%, 98.0%), and 97.7% (95% CI: 95.8%, 98.8%) for placing radiology reports into F_T and F_T0 categories and 99.6% (95% CI: 99.2%, 99.9%), 98.2% (95% CI: 95.0%, 99.6%), 99.9% (95% CI: 99.4%, 99.99%), 99.4% (95% CI: 96.3%, 99.9%), and 99.7% (95% CI: 98.9%, 99.9%) for placing reports into R_T and R_T0 categories.

CONCLUSION: LEXIMER is an accurate automated engine for evaluating the percentage positivity of clinically important findings and rates of recommendation for subsequent action in unstructured radiology reports.

© RSNA, 2004

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