Predicting pelvic trauma severity using features extracted from records and X-ray and CT images

Computer-aided decision making systems can assist physicians in prompt and accurate treatment of the high-energy pelvic trauma injuries by rapidly analyzing patient data and generating recommendations based on a large database of prior cases. However, no current system incorporates information contained in medical images. This paper presents a method which combines demographic information, standard medical measurements and features extracted from both X-ray images and Computed Tomography (CT) scans, to predict whether a patient will be sent to ICU after initial triage and stabilization. Predictions are presented in the form of rules, extracted from trees generated using the C4.5 algorithm. Results are promising and indicate that the image features are statistically significant in patient outcome prediction.