Can machine learning methods be used for identification of at-risk neonates in low-resource settings? A prospective cohort study.

ABSTRACT:

Introduction

Timely identification of at-risk neonates (ARNs) in the community is essential to reduce mortality in low-resource settings. Tools such as American Academy of Pediatrics pulse oximetry (POx) and WHO Young Infants Clinical Signs (WHOS) have high specificity but low sensitivity to identify ARNs. Our aim was assessing the value of POx and WHOS independently, in combination and with machine learning (ML) from clinical features, to detect ARNs in a low/middle-income country.

Methods

This prospective cohort study was conducted in a periurban community in Pakistan. Eligible live births were screened using WHOS and POx along with clinical information regarding pregnancy and delivery. The enrolled neonates were followed for 4 weeks of life to assess the vital status. The

SUBMITTER: Hasan BS 

PROVIDER: S-EPMC10626794 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature