PMID: 7702231May 1, 1995Paper

Unlocking clinical data from narrative reports: a study of natural language processing

Annals of Internal Medicine
G HripcsakP D Clayton

Abstract

To evaluate the automated detection of clinical conditions described in narrative reports. Automated methods and human experts detected the presence or absence of six clinical conditions in 200 admission chest radiograph reports. A computerized, general-purpose natural language processor; 6 internists; 6 radiologists; 6 lay persons; and 3 other computer methods. Intersubject disagreement was quantified by "distance" (the average number of clinical conditions per report on which two subjects disagreed) and by sensitivity and specificity with respect to the physicians. Using a majority vote, physicians detected 101 conditions in the 200 reports (0.51 per report); the most common condition was acute bacterial pneumonia (prevalence, 0.14), and the least common was chronic obstructive pulmonary disease (prevalence, 0.03). Pairs of physicians disagreed on the presence of at least 1 condition for an average of 20% of reports. The average intersubject distance among physicians was 0.24 (95% Cl, 0.19 to 0.29) out of a maximum possible distance of 6. No physician had a significantly greater distance than the average. The average distance of the natural language processor from the physicians was 0.26 (Cl, 0.21 to 0.32; not significantly g...Continue Reading

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