Public articles linked to the same research event.
Chinese medical journal Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.
Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.
Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.
Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.