ArticleBMC pediatrics2021
Neonatal mortality prediction with routinely collected data: a machine learning approach.
Article in BMC pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.BMC medical informatics and decision making · 2022Pooled it
- Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.Healthcare (Basel, Switzerland) · 2026Review
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- Interpretable prediction of neonatal mortality and its key predictors using machine learning and SHAP analysis.BMC medical informatics and decision making · 2026Article
- Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data.PLOS global public health · 2026Article
- Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.Population health metrics · 2025Article
- Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.Scientific reports · 2025Article
- The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.PLOS digital health · 2025Review
- Predictors of mortality among neonates in Lusaka, Zambia: a comparative analysis of machine learning and traditional survival analysis techniques.Frontiers in artificial intelligence · 2025Article
- Development and validation of a prognosis risk score model for neonatal mortality in the Amhara region, Ethiopia. A prospective cohort study.Global health action · 2024Article
- Predicting maternal risk level using machine learning models.BMC pregnancy and childbirth · 2024Article
- Predictive modeling of gestational weight gain: a machine learning multiclass classification study.BMC pregnancy and childbirth · 2024Article
- Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia.BMC pulmonary medicine · 2024Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Artificial neural network approaches to identify maternal and infant risk and asset factors using Peridata.Net: a WI-MIOS study.JAMIA open · 2023Article
- Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients.International journal of general medicine · 2023Article
- Artificial intelligence and machine learning in pediatrics and neonatology healthcare.Revista da Associacao Medica Brasileira (1992) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.
methodsA probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data.
resultsThe best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO's five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records.
conclusionMachine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
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