Predicting Pediatric Sepsis and Mortality Using Wearable Device Data and Machine Learning in Bangladesh.

ABSTRACT: Sepsis disproportionately impacts children in low-resource settings, where diagnostic tools like the Phoenix Sepsis Score (PSS) are constrained by reliance on laboratory testing. The objective of this research was to evaluate the use of continuous physiological data from low-cost wearable biosensors and machine learning models to predict pediatric sepsis, septic shock, and mortality in a low-resource, intensive care setting. This prospective observational single-site study analyzed 96 pediatric intensive care unit patients with suspected sepsis in Dhaka, Bangladesh. Physiological data were collected using a wearable biosensor patch, whereas clinical exams, laboratory tests, and PSS criteria identified sepsis, septic shock, and mortality. Least absolute shrinkage and selection operator (LAS

SUBMITTER: Jindal A 

PROVIDER: S-EPMC12756922 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature