Machine learning: the what, why, and how

Abstract

There is a growing interest in the ability of machine learning to predict outcomes in shoulder arthroplasty. Machine learning is the use of computer systems that can adapt and learn by using algorithms to draw statistical significance and predictions from data. Multiple studies have shown that machine learning algorithms can help predict postoperative outcomes, including patient-reported outcome measures (PROMs), range of motion, complications, and unplanned readmissions. Ultimately, the promise of machine learning in shoulder arthroplasty is to improve patient risk stratification, procedure selection, and long-term outcomes. Modern algorithms rely on numerous input variables, although recent efforts seek to reduce the required number of variables to improve efficiency and decrease the clinical burden. A review of recent machine learning studies allows for a deeper understanding of the potential use of predictive analytics in improving outcomes of shoulder arthroplasty.