This study aims to develop a natural language processing (NLP) tool to extract the pulmonary nodules and nodule characteristics information from free-text clinical narratives. We identified a cohort of 3,080 patients who received low dose computed tomography (LDCT) at the University of Florida health system and collected their clinical narratives including radiology reports in their electronic health records (EHRs). Then, we manually annotated 394 reports as the gold-standard corpus and explored three state-of-the-art transformer-based NLP methods. The best model achieved an F1-score of 0.9279.
Keywords: deep learning; natural language processing; nodule characteristics; pulmonary nodule.