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After radiologists perform a set of chest-x-rays (CXRs) they write a short report describing their observations and interpretations. Because these reports are free-text documents, there is the risk of miscommunication, which can result in reduced patient outcomes. We applied text mining methods to radiology reports in the MIMIC Chest X-ray (MIMIC-CXR) database [5], consisting of 227,835 de-identified free-text radiology reports. We selected relevant terms (features) and developed predictive models that take a radiology report as input and return the probability the report describes a positive diagnosis for pneumonia, a common respiratory condition characterized by the accumulation of fluid in the lungs. Subsequently, we evaluated the performance of different predictive models using the area under the curve (AUC) and the Brier Score.

Due to the large number of reports in the MIMIC-CXR database, we generated and …