Additional Value of Augmenting Current Subscales in Braden Scale with Advanced Machine Learning Technique for Pressure Injury Risk Assessment

摘要

Hospital-acquired pressure injuries (PI) are associated with longer hospital stays, pain, infection, and higher care costs. The traditional assessment techniques such as Braden scale, the most widely used PI risk assessment tool, lack predictive power. This study implements a machine learning algorithm using XGBoost and Braden subscales as its input features for PI risk assessment in intensive unit care (ICU) patients. We have evaluated our proposed PI risk assessment algorithm on a test dataset of 2,657 patients (PI prevalence equals to 17.57%) and have obtained 5.9% and 3.1% improvement in sensitivity and specificity respectively for our machine learning-based approach compared to the Braden scale.