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

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 Braz...

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Auteurs principaux:	Thiago Augusto Hernandes Rocha, Erika Bárbara Abreu Fonseca de Thomaz, Dante Grapiuna de Almeida, Núbia Cristina da Silva, Rejane Christine de Sousa Queiroz, Luciano Andrade, Luiz Augusto Facchini, Marcos Luiggi Lemos Sartori, Dalton Breno Costa, Marcos Adriano Garcia Campos, Antônio Augusto Moura da Silva, Catherine Staton, João Ricardo Nickenig Vissoci
Format:	article
Langue:	EN
Publié:	Elsevier 2021
Sujets:	Preterm Birth Predictive Value of Tests Primary Health Care Appraisal, Health Risk, Machine Learning Public aspects of medicine RA1-1270
Accès en ligne:	https://doaj.org/article/a7a8f971ee9d4de898f1f5399ba79c3e
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Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach

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