Eun-Kyoung Goh, Hyo-Jeong Jeon
Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students
Journal of Intelligence
postpartum haemorrhage pph significant cause maternal morbidity mortality worldwide particularly lowresource settings study aimed develop predictive model pph using early risk factors rank importance terms predictive ability dataset obtained observational casecontrol study northern rwanda various statistical models machine learning techniques evaluated including logistic regression logistic regression elasticnet regularisation random forests extremely randomised trees gradientboosted trees xgboost random forest model average sensitivity 807 specificity 713 misclassification rate 1219 outperformed models demonstrating potential reliable tool predicting pph important predictors identified study haemoglobin level labour maternal age however differences pph risk factor importance different data partitions highlighting need investigation findings contribute understanding pph risk factors highlight importance considering different data partitions implementing crossvalidation predictive modelling emphasise value identifying appropriate prediction model application effective pph prediction models essential improving maternal health outcomes global scale study provides valuable insights healthcare providers develop predictive models pph identify highrisk women implement targeted interventions