A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings frequently relies on manual review of textual radiology reports and can be overlooked in a busy clinical environment. Our study aimed to develop and validate a supervised machine learning model using natural language processing to automate the recognition of incidental findings in radiology reports of patients discharged from the ED. METHODS: We performed a retrospective analysis of computed tomography (CT) reports from trauma patients discharged home across an integrated health system in 2019. Two independent annotators manually labeled CT reports for the presence of an incidental finding as a reference standard. We used regular expressions to derive and validate a random forest model using open-source and machine learning software. Final model performance was assessed across different ED types. RESULTS: The study CT reports were divided into derivation (690 reports) and validation (282 reports) sets, with a prevalence of incidental findings of 22.3%, and 22.7%, respectively. The random forest model had an area under the curve of 0.88 (95% confidence interval [CI], 0.84 to 0.92) on the derivation set and 0.92 (95% CI, 0.88 to 0.96) on the validation set. The final model was found to have a sensitivity of 92.2%, a specificity of 79.4%, and a negative predictive value of 97.2%. Similarly, strong model performance was found when stratified to a dedicated trauma center, high-volume, and low-volume community EDs. CONCLUSION: Machine learning and natural language processing can classify incidental findings in CT reports of ED patients with high sensitivity and high negative predictive value across a broad range of ED settings. These findings suggest the utility of natural language processing in automating the review of free-text reports to identify incidental findings and may facilitate interventions to improve timely follow-up.

Computational analysis of swallowing mechanics after surgery for obstructive sleep apnea.

Multilevel upper airway surgery for obstructive sleep apnea (OSA) has been shown to cause clinically significant dysphagia in some patients. We describe the cases of 2 adults with OSA who developed persistent dysphagia after multilevel upper airway surgery. Patient-specific computational analysis of swallowing mechanics (CASM) revealed absent pharyngeal shortening and aberrant tongue base retraction in both patients. These findings are consistent with the OSA surgical goal of enlarging the hypopharyngeal airway but likely contributed to our patients' dysphagia. Patient-specific CASM allows for sensitive identification of swallowing mechanical dysfunction that might otherwise be overlooked, and it may be utilized in future head and neck surgery patients to analyze swallowing dysfunction associated with treatment.