Improving preterm newborn identification in low-resource settings with machine learning

Katelyn J. Rittenhouse, Bellington Vwalika, Alexander Keil, Jennifer Winston, Marie Stoner, Joan T. Price, Monica Kapasa, Mulaya Mubambe, Vanilla Banda, Whyson Muunga, Jeffrey S. A. Stringer, Chelsea Dobbins
2019 PLoS ONE  
Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using
more » ... rn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.
doi:10.1371/journal.pone.0198919 pmid:30811399 pmcid:PMC6392324 fatcat:3sjb4viqqjdp3a34kalcpfnq7m