Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty.
Sai K DevanaAkash A ShahChanghee LeeVarun GudapatiAndrew R JensenEdward CheungCarlos SolorzanoMihaela van der SchaarNelson F SooHooPublished in: Journal of shoulder and elbow arthroplasty (2021)
Our study reports an ML model for the prediction of major complications or 30-day readmission following rTSA. XGBoost outperformed traditional LR models and also identified key predictive features of complications and readmission.