Machine Learning–Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study

The useful part

Neonatal sepsis remains a major cause of neonatal morbidity and mortality in low- and middle-income countries (LMICs). Early diagnosis is challenging because of nonspecific clinical manifestations and delays in laboratory confirmation. Machine learning (ML) approaches using structured electronic health record (EHR) data may improve early risk stratification in neonatal intensive care units (NICUs).

How it works

• This study aimed to evaluate ML models for predicting culture-confirmed neonatal sepsis among neonates admitted to a tertiary NICU in Jordan, with the objective of addressing diagnostic gaps in...
• The dataset was divided into training (n=2619, 80%) and testing (n=655, 20%) subsets using stratified sampling.
• Three ML models—Extreme Gradient Boosting (XGBoost), decision trees, and neural networks—were trained using clinical, laboratory, and demographic variables.
• Class imbalance was addressed using the synthetic minority oversampling technique (SMOTE) applied to the training dataset.
• Methods: A retrospective cohort study was conducted using structured EHRs of 3274 neonates admitted to a tertiary NICU in Jordan between 2018 and 2024.

What to take from it

Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).

Details worth keeping

Neonates who underwent blood culture testing were included.