Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach.

ABSTRACT:

Background

Preterm birth (PTB) is a growing health issue worldwide, currently considered the leading cause of newborn deaths. To address this challenge, the present work aims to develop an algorithm capable of accurately predicting the week of delivery supporting the identification of a PTB in Brazil.

Methods

This a population-based study analyzing data from 3,876,666 mothers with live births distributed across the 3,929 Brazilian municipalities. Using indicators comprising delivery characteristics, primary care work processes, and physical infrastructure, and sociodemographic data we applied a machine learning-based approach to estimate the week of delivery at the point of care level. We tested six algorithms: eXtreme Gradient Boosting, Elastic Net, Quantile Ordinal Regress

SUBMITTER: Rocha TAH 

PROVIDER: S-EPMC9904131 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature