ArticleLancet regional health. Americas2021
Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach.
Article in Lancet regional health. Americas, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Risk of Preterm Birth in Women With Periodontitis: A Systematic Review and Meta-Analysis.International journal of dental hygiene · 2026Pooled it
- Artificial intelligence technologies in pregnancy and childbirth care: a scoping review from a nursing perspective.Women's health nursing (Seoul, Korea) · 2026Review
- Temporal trends and factors associated with live preterm births in Espírito Santo, 2012-2021.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Machine learning-augmented biomarkers in mid-pregnancy Down syndrome screening improve prediction of small-for-gestational-age infants.Orphanet journal of rare diseases · 2025Article
- Predicting preterm birth using machine learning methods.Scientific reports · 2025Article
- Artificial intelligence in Brazilian Primary Health Care: scoping review.Revista brasileira de enfermagem · 2025Article
- Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.JMIR public health and surveillance · 2024Article
- Prediction of deciduous teeth eruption in Brazilian children: A cross-sectional study nested in a prospective birth cohort (BRISA).BMC oral health · 2024Article
- A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery.International journal of women's health · 2024Review
- PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks.BioData mining · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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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 Regression - LASSO, Linear Regression, Ridge Regression and Decision Tree. We used the root-mean-square error (RMSE) as a precision. Findings: All models obtained RMSE indexes close to each other. The lower levels of RMSE were obtained using the eXtreme Gradient Boosting approach which was able to estimate the week of delivery within a 2.09 window 95%IC (2.090-2.097). The five most important variables to predict the week of delivery were: number of previous deliveries through Cesarean-Section, number of prenatal consultations, age of the mother, existence of ultrasound exam available in the care network, and proportion of primary care teams in the municipality registering the oral care consultation. Interpretation: Using simple data describing the prenatal care offered, as well as minimal characteristics of the pregnant, our approach was capable of achieving a relevant predictive performance regarding the week of delivery. Funding: Bill and Melinda Gates Foundation, and National Council for Scientific and Technological Development - Brazil, (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ acronym in portuguese) Support of the research project named: Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care- Grant: OPP1202186.
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