SynthesisBMC medical informatics and decision making2022
On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.
Synthesis in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.Scientific reports · 2026Article
- Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol.Systematic reviews · 2026Article
- Using artificial intelligence as a technological tool in gynecologic and obstetric health: A narrative literature review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Doppler Assessment of the Fetal Brain Circulation.Diagnostics (Basel, Switzerland) · 2026Review
- Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data.PLOS global public health · 2026Article
- Machine learning for preventing stillbirths: is it possible to transform data into life-saving insights?BMC pregnancy and childbirth · 2025Article
- Article
- Application of machine learning in identifying risk factors for low APGAR scores.BMC pregnancy and childbirth · 2025Article
- Artificial Intelligence for Women and Child Healthcare: Is AI Able to Change the Beginning of a New Story? A Perspective.Health science reports · 2025Article
- Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.BMC public health · 2025Article
- Advances in AI and machine learning for predictive medicine.Journal of human genetics · 2024Review
- Bridging Data Gaps: Predicting Sub-national Maternal Mortality Rates in Kenya Using Machine Learning Models.Cureus · 2024Article
- Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses-Porto retrospective intrapartum study.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCare during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models.
methodsWe conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source.
resultsFrom 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios.
conclusionBased on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.
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Registered trials
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.