biostudies-literatureMohammed HZimmer BiometSyapse IncNIMHD NIH HHSSGM Consulting LLCNational Institute on Minority Health and Health Disparities of the National Institutes of Healthe0263897https://www.ebi.ac.uk/biostudies/studies/S-EPMC8939835biostudies-literatureUnknown17(3)<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>GBM-based predictive models were observedPloS oneUtilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty.PMC8939835R01 MD013901R01MD013901Memtsoudis SHuang YParks MMa YMohammed HfalseUtilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty.<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>GBM-based predictive models were observed2022-01-01T00:00:00Z20222025-04-21T16:45:24.531Z2025-04-21T16:45:24.531ZS-EPMC89398353531627010.1371/journal.pone.0263897