Results of network meta-analysis. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Results of network meta-analysis. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Results of network meta-analysis. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Results of network meta-analysis. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Study flowchart. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Risk of bias graph. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Results of network meta-analysis. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

Characteristics of included studies. حسب Kaiyu Zhang (198799)

"…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …"

An overview of the experimental paradigm. حسب Nienke E. R. van Bueren (11414330)

Supplementary Material for: Prediction of 28-day all-cause mortality in heart failure patients with Clostridioides difficile infection using machine learning models: evidence from... حسب Shi C. (4131748)

"…And nine machine learning (ML) algorithms, including logistic regression (LR), decision tree (DT), bayesian, adaptive boosting (AdaBoost), random forest (RF), gradient boosting decision tree (GBDT), XGBoost, light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) were applied for model construction. …"

Data Sheet 1_Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data.docx حسب Renjie Lu (2021845)

"…The Gradient Boosting Machine (GBM) outperformed other machine learning models. Applying Bayesian optimization to the GBM achieved an AUC of 0.933 (95% CI: 0.913–0.953) on the training set and 0.932 (95% CI: 0.905–0.960) on the validation set. …"