Application of Machine Learning to Prediction of Surgical Site Infection

Abstract

Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducted in rural Rwanda for the purpose of predicting infection in Cesarean section wounds, which is a leading cause of maternal mortality. Questionnaire and image data were collected from 572 mothers approximately 10 days after surgery at a district hospital. Of the 572 women, 61 surgical wounds were determined to be infected as determined by a physical exam conducted by trained doctors. Machine learning models, logistic regression and Support Vector Machines (SVM), were developed independently for the questionnaire data and the image data. For the questionnaire data, the best results were achieved by the Logistic regression model, with an AUC Accuracy = 96.50% (93.0%-99.3%), Sensitivity = 0.71 (0.33 – 0.92), and Specificity = 0.99 (0.98 – 1.00). The features with the greatest predictive value were the presence of malcolored drainage from the wound and the presence of an odorous discharge from the wound. Using the image data alone, the SVM model performed best, with an AUC Accuracy = 99.5% (99.2%-100%), Sensitivity = 0.99 (0.99 – 1.00), and Specificity = 0.99 (0.99 – 1.00). Combining both questionnaire data and image data, the SVM model achieved an AUC Accuracy = 99.9% (99.7%-100%), Sensitivity = 0.99 (0.99 –1.00), and Specificity = 0.99 (0.99 – 1.00). Results from this initial study are very encouraging and demonstrate that good objective prediction of surgical infection for women in rural Rwanda is feasible using machine learning, even when using image data alone.