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Updated: Sep 30, 2026
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic
George Hripcsak1, John H M Austin, Philip O Alderson
1Department of Medical Informatics, Columbia University, 622 W 168th St, VC-5, New York, NY 10032, USA. hripcsak@columbia.edu
Purpose:
To evaluate translation of chest radiographic reports by using natural language processing and to compare the findings with those in the literature.
Materials And Methods:
A natural language processor coded 10 years of narrative chest radiographic reports from an urban academic medical center. Coding for 150 reports was compared with manual coding. Frequencies and co-occurrences of 24 clinical conditions (diseases, abnormalities, and clinical states) were estimated. The ratio of right to left lung mass, association of pleural effusion with other conditions, and frequency of bullet and stab wounds were compared with independent observations. The sensitivity and specificity of the system's pneumothorax coding were compared with those of manual financial coding.
Results:
The system coded 889,921 reports on 251,186 patients. On the basis of manual coding of 150 reports, the processor's sensitivity (0.81) and specificity (0.99) were comparable to those previously reported for natural language processing and for expert coders. The frequencies of the selected conditions ranged from 0.22 for pleural effusion to 0.0004 for tension pneumothorax. The database confirmed earlier observations that lung cancer occurs in a 3:2 right-to-left ratio. The association of pleural effusion with other conditions mirrored that in the literature. Bullet and stab wounds decreased during 10 years at a rate consistent with crime statistics. A review of pneumothorax cases showed that the database (sensitivity, 1.00; specificity, 0.996) was more accurate than financial discharge coding (sensitivity, 0.17; P =.002; specificity, 0.996; not significant).
Conclusion:
Internal and external validation in this study confirmed the accuracy of natural language processing for translating chest radiographic narrative reports into a large database of information.
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