A methodological approach to validate pneumonia encounters from radiology reports using Natural Language Processing (NLP)

Authors

Aloksagar Panny, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Harshad Hegde, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Ingrid Glurich, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Frank A. Scannapieco, Department of Oral Biology, School of Dental Medicine, State University of New York at Buffalo, Buffalo, United States.
Jayanth G. Vedre, Critical Care Medicine Department, Marshfield Clinic Health System, Marshfield, United States.
Jeffrey J. VanWormer, Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, United States.
Jeffrey Miecznikowski, Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, United States.
Amit Acharya, Advocate Aurora HealthFollow

Recommended Citation

Panny A, Hegde H, Glurich I, et al. A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing. Methods Inf Med. 2022;61(1-02):38-45. doi:10.1055/a-1817-7008

Affiliations

Advocate Aurora Research Institute

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 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.

Document Type

Article

PubMed ID

35381617