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Fig. 2 | BMC Emergency Medicine

Fig. 2

From: Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department

Fig. 2

This figure presents calibration plots for multiple predictive models assessing their performance in predicting (A) primary and (B) secondary outcomes in the CMUH test set. Each model's Brier score, indicating the accuracy of probabilistic predictions, is displayed along with 95% CI. The shaded areas represent the 95% CI for each model's calibration curve. CatBoost, Categorical Boosting; CI, confidence intervals; CMUH, China Medical University Hospital; ET, Extremely Randomized Trees; GB, Gradient Boosting; LGB, Light Gradient Boost Machine; LR, Logistic Regression; RF, Random Forest; LR-TTAS, Logistic Regression-Taiwan Triage Acuity scale

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BMC Emergency Medicine

ISSN: 1471-227X

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