Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning
Abbas M. Hassan, Andrea Biaggi-Ondina, Aashish Rajesh, Malke Asaad, Jonas A. Nelson, J. Henk Coert, Babak J. Mehrara, Charles E. Butler
Abstract
Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.