Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach

[1] Chen YS(2008)Cardiopulmonary resuscitation with assisted extracorporeal life-support versus conventional cardiopulmonary resuscitation in adults with in-hospital cardiac arrest: an observational study and propensity analysis Lancet 372 554-561

[2] Lin JW(2016)Survival analysis after extracorporeal membrane oxygenation in critically Ill Adults: a nationwide cohort study Circulation 133 2423-2433

[3] Yu HY(2019)Survival analysis of extracorporeal membrane oxygenation in neonatal and pediatric patients-a nationwide cohort study J Formos Med Assoc 118 1339-1346

[4] Ko WJ(2018)Impact of age-adjusted charlson comorbidity on hospital survival and short-term outcome of patients with extracorporeal cardiopulmonary resuscitation J Clin Med 7 313-434

[5] Jerng JS(2019)Dalton H, Eunice Kennedy Shriver National Institute of Child H and Human Development Collaborative Pediatric Critical Care Research N Development of the Pediatric Extracorporeal Membrane Oxygenation Prediction Model for Risk-Adjusting Mortality Pediatr Crit Care Med 20 426-2256

[6] Chang WT(2017)Comparison of mortality prediction models in acute respiratory distress syndrome undergoing extracorporeal membrane oxygenation and development of a novel prediction score: the PREdiction of Survival on ECMO Therapy-Score (PRESET-Score) Crit Care 21 301-105

[7] Chen WJ(2015)Predicting survival after ECMO for refractory cardiogenic shock: the survival after veno-arterial-ECMO (SAVE)-score Eur Heart J 36 2246-1425

[8] Huang SC(2020)Risk prediction of in-hospital mortality in patients with venoarterial extracorporeal membrane oxygenation for cardiopulmonary support: The ECMO-ACCEPTS score J Crit Care 56 100-210

[9] Chi NH(2019)Artificial intelligence prediction model for the cost and mortality of renal replacement therapy in aged and super-aged populations in Taiwan J Clin Med 8 995-442

[10] Wang CH(2020)Prediction of all-cause mortality in haemodialysis patients using a Bayesian network Nephrol Dial Transplant 35 1420-1825

[1] Chen YS(2008)Cardiopulmonary resuscitation with assisted extracorporeal life-support versus conventional cardiopulmonary resuscitation in adults with in-hospital cardiac arrest: an observational study and propensity analysis Lancet 372 554-561

[2] Lin JW(2016)Survival analysis after extracorporeal membrane oxygenation in critically Ill Adults: a nationwide cohort study Circulation 133 2423-2433

[3] Yu HY(2019)Survival analysis of extracorporeal membrane oxygenation in neonatal and pediatric patients-a nationwide cohort study J Formos Med Assoc 118 1339-1346

[4] Ko WJ(2018)Impact of age-adjusted charlson comorbidity on hospital survival and short-term outcome of patients with extracorporeal cardiopulmonary resuscitation J Clin Med 7 313-434

[5] Jerng JS(2019)Dalton H, Eunice Kennedy Shriver National Institute of Child H and Human Development Collaborative Pediatric Critical Care Research N Development of the Pediatric Extracorporeal Membrane Oxygenation Prediction Model for Risk-Adjusting Mortality Pediatr Crit Care Med 20 426-2256

[6] Chang WT(2017)Comparison of mortality prediction models in acute respiratory distress syndrome undergoing extracorporeal membrane oxygenation and development of a novel prediction score: the PREdiction of Survival on ECMO Therapy-Score (PRESET-Score) Crit Care 21 301-105

[7] Chen WJ(2015)Predicting survival after ECMO for refractory cardiogenic shock: the survival after veno-arterial-ECMO (SAVE)-score Eur Heart J 36 2246-1425

[8] Huang SC(2020)Risk prediction of in-hospital mortality in patients with venoarterial extracorporeal membrane oxygenation for cardiopulmonary support: The ECMO-ACCEPTS score J Crit Care 56 100-210

[9] Chi NH(2019)Artificial intelligence prediction model for the cost and mortality of renal replacement therapy in aged and super-aged populations in Taiwan J Clin Med 8 995-442

[10] Wang CH(2020)Prediction of all-cause mortality in haemodialysis patients using a Bayesian network Nephrol Dial Transplant 35 1420-1825