AI-Driven Early Prediction of Neonatal Sepsis Using Clinical and Laboratory  Parameters: A Public Health-Oriented Neonatal Risk Assessment Model

Lubna Manzoor, Arooj Haider, Javaria Younus, Tahir Mahmood Butt, Zille Huma Mustehsan, Shahab Ali

AI-Driven Early Prediction of Neonatal Sepsis Using Clinical and Laboratory Parameters: A Public Health-Oriented Neonatal Risk Assessment Model

Authors

Lubna Manzoor, Arooj Haider, Javaria Younus, Tahir Mahmood Butt, Zille Huma Mustehsan, Shahab Ali

Keywords:

Neonatal sepsis; Artificial intelligence; Risk prediction model; Machine learning; Neonatal intensive care

Abstract

Neonatal sepsis remains a major contributor to neonatal morbidityand mortalityworldwide, particularly in low- and middleincome countries, where delayed diagnosisand limited neonatal intensive care resources

References

1. Shane AL, Sánchez PJ, Stoll BJ. Neonatal sepsis. Lancet. 2023;390(10104):1770-1780. DOI: https://doi.org/10.1016/S0140 6736(17)31002-4

Downloads

Published

2026-05-25

How to Cite

Lubna Manzoor, Arooj Haider, Javaria Younus, Tahir Mahmood Butt, Zille Huma Mustehsan, Shahab Ali. (2026). AI-Driven Early Prediction of Neonatal Sepsis Using Clinical and Laboratory Parameters: A Public Health-Oriented Neonatal Risk Assessment Model . International Journal of Pharmacy Research & Technology (IJPRT), 16(1), 3054–3060. Retrieved from https://ijprt.org/index.php/pub/article/view/1981