Skip to main content

Table 1 Significant scholarly works that ML techniques to compare with the performance of APACHE II

From: Improvement of APACHE II score system for disease severity based on XGBoost algorithm

Study Data source Condition Number of patients Machine learning algorithms Accuracy AUC
Samaneh Layeghian Javan et al.[19] MIMIC III Cardiac arrest 4611 Stacking algorithm 0.76 0.82
Min Woo Kang et al.[20] Seoul National University Hospital Continuous renal replacement therapy 1571 Random forest / 0.78
Meng Hsuen Hsieh et al. [21] Chi-Mei Medical Center Patients with unplanned extubation in intensive care units 341 Random forest 0.88 0.91
Zhongheng Zhang et al.[22] SAILS study and OMEGA study Acute respiratory distress syndrome 1071 Neural network / 0.821
Dan Assaf et al.[21] Sheba Medical Center Coronavirus disease (COVID-19) 162 Random forest 0.92 0.93
Grupo de Trabajo Gripe A Grave et al.[23] GETGAG/SEMICYUC database Severe influenza 3959 Random forest 0.83 0.82
Kuo-Ching Yuan et al.[24] Taipei Medical University Hospital Sepsis 434 XGBoost 0. 82 0.89
Scherpf M et al.[39] MIMIC III Sepsis 1050 Recurrent neural network / 0.81
Zhang Z et al.[40] MIMIC III Acute kidney injury 6682 XGBoost / 0.86
Kong G et al.[41] MIMIC III Sepsis 16,688 Gradient boosting machine   0.85
Our work MIMIC III ICU patients 24,777 XGBoost 0.87 0.81