Traumatic Pelvic Injury Outcome Prediction by Extracting Features from Relevant Medical Records and X-Ray Images

Abstract

Traumatic pelvic injuries are complex and difficult to treat, due to the high risk of complications. Prompt and accurate medical treatment is therefore vital. Computeraided decision-making systems can assist physicians in this task, but none of those proposed so far incorporate features extracted from medical images. The study in this paper uses demographic information, standard medical measurements, and features extracted from X-ray images to predict a patient's length of stay in ICU via rules extracted from decision trees generated by the CART algorithm. The X-ray features are extracted by using a spline/ASM segmentation technique to detect structure position, then calculating measures of displacement. The results are promising and compare well with SVM and C4.5 algorithms, indicating that the rules represent true data patterns. Significantly, an X-ray feature is selected as highly important to injury severity, indicating that medical image features are important in providing accurate recommendations and predictions.