A systematic literature review of predictive analytics methods for early diagnosis of neonatal sepsis

Neonatal sepsis is a severe medical condition that contributes significantly to neonatal mortality. However, early diagnosis and treatment can help manage the condition effectively. Predictive analytics can assist neonatal sepsis diagnosis and treatment and offer a reassuring solution. This study presents a systematic literature review of various predictive analytics methods for neonatal sepsis diagnosis and treatment. It thoroughly reviews 16 studies between 2014 and 2024, including retrospective and prospective data and utilizing various predictive modeling techniques, such as Machine Learning (ML) and Deep Learning (DL). The review unveils that the Predictive analytics models can rapidly detect early and late-onset neonatal sepsis. This detection aids clinicians in decision-making and improves healthcare management for neonates, especially in low-resource settings. This study lays the groundwork for utilizing advanced analytics technologies to address challenges in this domain. It inspires further research in innovative and unexplored methods for neonatal sepsis diagnosis.