Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty.

ABSTRACT:

Background

Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources.

Methods

The National Inpatient Sample datasets from 2010-2014 were used to build Logistic Regression (LR), Gradient Boosting Method (GBM), Random Forest (RF), and Artificial Neural Network (ANN) predictive models for three clinically relevant outcomes after TKA-disposition at discharge, any post-surgical complications, and blood transfusion. Model performance was evaluated using the Brier scores as calibration measures, and area under the ROC curve (AUC) and F1 scores as discrimination measures.

Results

GBM-based predictive models were observed

SUBMITTER: Mohammed H 

PROVIDER: S-EPMC8939835 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature