Machine Learning Risk Assessment Model for Hospital Acquired Pressure Injuries

Hospital Acquired Pressure Injuries (HAPI) adversely affect patient outcomes, increase health care costs, and despite considerable efforts to avoid, are increasing among critically ill patients. Many factors contribute to the development and advancement of pressure injuries in acute and critical care settings. Manual assessment tools, such as the Braden Scale, are currently utilized to predict HAPIs. This project aims to generate a model to predict HAPIs with better accuracy than the Braden Scale using a machine learning approach. Data sets were developed so predictions could be made after 48-hours from admission and 72-hours from admission. Through quantitative evaluation of the Receiver Operating Characteristic (ROC) curve, the Explainable Boosting Machine (EBM) algorithm produced a model with a greater area under the ROC curve (AUC) of 0.79 while the Braden Scale produced an AUC of 0.70 at both 48-hours and 72-hours.Master of Science in Information Scienc