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A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language ProcessingFunding Research reported in this publication was supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health under Award Number 1R03DE027020–01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Introduction Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format.
Objective The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format.
Methods A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature–specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: “positive,” “negative,” or “not classified: requires manual review” based on tagged concepts that support or refute diagnostic codes.
Results A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as “Pneumonia-positive,” 19% as (15401/81,707) as “Pneumonia-negative,” and 48% (39,209/81,707) as “episode classification pending further manual review.” NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%).
Conclusion The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.
Received: 17 August 2021
Accepted: 02 April 2022
Accepted Manuscript online:
05 April 2022
Article published online:
19 August 2022
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