NLP-based Identification of Pneumonia Cases from Free-Text Radiological Reports

Peter L. Elkin, MD, David Froehling, MD, Dietlind Wahner-Roedler, MD, Brett Trusko, PhD, Gail Welsh, MD, Haobo Ma, MD, Armen X. Asatryan, MD, Jerome I. Tokars, MD, S. Trent Rosenbloom, MD, MPH, Steven H. Brown, MD Radiological reports are a rich source of clinical data which can be mined to assist with biosurveillance of emerging infectious diseases. In addition to biosurveillance, radiological reports are an important source of clinical data for health service research. Pneumonias and other radiological findings on chest xray or chest computed tomography are one type of relevant finding to both biosurveillance and health services research. In this study we examined the ability of a Natural Language Processing system to accurately identify pneumonias and other lesions from within free-text radiological reports. The system encoded the reports in the SNOMED CT Ontology and then a set of SNOMED CT based rules were created in our Health Archetype Language aimed at the identification of these radiological findings and diagnoses. The encoded rule was executed against the SNOMED CT encodings of the radiological reports. The accuracy of the reports was compared with a Clinician review of the Radiological Reports. The accuracy of the system in the identification of pneumonias was high with a sensitivity (recall) of 100%, a specificity of 98%, and a positive predictive value (precision) of 97%. We conclude that SNOMED CT based computable rules are accurate enough for the automated biosurveillance of pneumonias from radiological reports.