ArticleBMJ open2020
Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled 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
- Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data.PLOS global public health · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Machine learning to predict the role of CHWs in shifting birth preferences away from homebirth in India.Scientific reports · 2025Article
- The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.PLOS digital health · 2025Review
- Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.Pediatric surgery international · 2025Article
- Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.Scientific reports · 2025Article
- Prediction of neonatal death in pregnant women in an intensive care unit: Application of machine learning models.Heliyon · 2023Article
- Identifying risk of stillbirth using machine learning.American journal of obstetrics and gynecology · 2023Article
- Predicting facility-based delivery in Zanzibar: The vulnerability of machine learning algorithms to adversarial attacks.Heliyon · 2023Article
- Application of machine learning to identify risk factors of birth asphyxia.BMC pregnancy and childbirth · 2023Article
- Multi-omics and machine learning for the prevention and management of female reproductive health.Frontiers in endocrinology · 2023Review
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
- Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.Yonsei medical journal · 2022Article
- Early neonatal mortality is modulated by gestational age, birthweight and fetal heart rate abnormalities in the low resource setting in Tanzania - a five year review 2015-2019.BMC pediatrics · 2022Article
- Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning for maternal health: Predicting delivery location in a community health worker program in Zanzibar.Frontiers in digital health · 2022Article
- Prediction of neonatal deaths in NICUs: development and validation of machine learning models.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveWe aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.
designA secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.
settingThe KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.
participantsSingleton deliveries (n=42 319) with complete records from 2000 to 2015. PRIMARY OUTCOME MEASURES: Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.
resultsThe proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values.
conclusionsIn this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.