Radiologic imaging of trauma patients often uncovers findings that are unrelated to the trauma. Identifying these incidental findings in clinical notes is necessary for proper follow-up. We developed and evaluated an automated pipeline to identify incidental findings in radiology reports of trauma patients at the sentence and section levels using a variety of feature representations. We annotated a corpus of over 4,000 reports and investigated several feature representations including traditional word and concept (such as SNOMED-CT) representations as well as word and concept embeddings. We evaluated these representations using traditional machine learning as well as CNN-based deep learning methods. Our results show that the best performance was achieved by using CNNs with Pre-trained embedding at both sentence and section levels. This provides evidence that such a pipeline is likely to be clinically useful to identify incidental findings in radiology reports in trauma patients