A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.

ABSTRACT: Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked

SUBMITTER: Garbern SC 

PROVIDER: S-EPMC11524492 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature