Pain research & management
Development and Validation of a Machine Learning-Based Prediction of Chronic Postsurgical Pain Following Total Hip and Knee Arthroplasty: An Exploratory Study
Par Cao Y, Hu Y, Gong T et 3 autres.
Contenu en anglais
Résumé
Introduction
Chronic postsurgical pain (CPSP) is a concern following total hip arthroplasty (THA) and total knee arthroplasty (TKA), substantially diminishing surgical benefits. We aimed to develop a machine learning (ML) prediction model for risk of developing CPSP.
Methods
This cohort study included 383 patients recruited from a tertiary medical center between March 2022 and July 2024. Dataset was divided into a training set and a testing set. We used Lasso regression for feature selection and developed prediction models using six ML algorithms: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBOOST), support vector machine (SVM), multilayer perceptron (MLP), and k-nearest neighbor (KNN). Model performance was evaluated using metrics such as receiver operator characteristic (ROC) curve and precision-recall (PR) curves, accuracy, sensitivity, and specificity. The optimal model was deployed on a web application using Shiny, and Shapley additive explanations (SHAP) analysis was used to interpret model predictions.