A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.

ABSTRACT:

Background

There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA.

Methods

Thirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic re

SUBMITTER: Devana SK 

PROVIDER: S-EPMC8349766 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature