Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.
Manuel B HuberChristoph F KurzReiner LeidlPublished in: BMC medical informatics and decision making (2019)
Supervised machine-learning implementations, like extreme gradient boosting, can provide better performance than linear models and should be considered, when high predictive performance is needed. Preoperative VAS, Q score and specific dimensions like limping are the most important predictors for postoperative hip and knee PROMs.