Ying Yan Zhong, Angelyn Anton, Owen Xie, Natalie Tan, Sophie O'Haire, Sam Maleki, Andrisha-Jade Inderjeeth, Phillip Parente, Lavinia Spain, Peter Gibbs, Ben Tran
Impact of Comorbidities and Drug Interactions in Patients With Metastatic Castration-Resistant Prostate Cancer Receiving Androgen Receptor Pathway Inhibitors
JCO oncology practice
growing concern pediatric mortality demands heightened preparedness clinical settings especially within intensive care units icus respiratoryrelated admissions account substantial portion pediatric illnesses pressing need predict icu mortality cases study based data 1188 patients addresses imperative using machine learning techniques investigating different class balancing methods pediatric icu mortality prediction study employs publicly accessible paediatric intensive care database train validate test machine learning model predicting pediatric patient mortality features ranked using three machine learning feature selection techniques namely random forest extra trees xgboost resulting selection 16 critical features total 105 features ten machine learning models ensemble techniques used make accurate mortality predictions tackle inherent class imbalance dataset applied unique data partitioning technique enhance models alignment data distribution catboost machine learning model achieved area curve auc 7222 stacking ensemble model yielded auc 6059 mortality prediction proposed subdivision technique hand provides significant improvement performance metrics auc 852 accuracy 8932 findings emphasize potential machine learning enhancing pediatric mortality prediction inform strategies improved icu readiness