AlokSagar Panny1, Harshad Hegde1, Ingrid Glurich1, Frank A Scannapieco2, Jayanth G Vedre3, Jeffrey J VanWormer4, Jeffrey Miecznikowski5, Amit Acharya1,6. 1. Center for Oral-Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States. 2. Department of Oral Biology, School of Dental Medicine, State University of New York at Buffalo, Buffalo, New York, United States. 3. Department of Critical Care Medicine, Marshfield Clinic Health System, Marshfield, Wisconsin, United States. 4. Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States. 5. Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, New York, United States. 6. Advocate Aurora Research Institute, Advocate Aurora Health, Downers Grove, Illinois, United States.
Abstract
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. Thieme. All rights reserved.
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. Thieme. All rights reserved.
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Authors: Harshad Hegde; Ingrid Glurich; Aloksagar Panny; Jayanth G Vedre; Jeffrey J VanWormer; Richard Berg; Frank A Scannapieco; Jeffrey Miecznikowski; Amit Acharya Journal: Methods Inf Med Date: 2022-03-17 Impact factor: 1.800
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