ArticleBMC medical informatics and decision making2021
Prediction of neonatal deaths in NICUs: development and validation of machine learning models.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain.Frontiers in artificial intelligence · 2026Pooled it
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled 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
- [Establishment of a predictive nomogram model for predicting the death of very preterm infants during hospitalization].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2022Trial
- 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
- Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol.Systematic reviews · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Optimized ensemble learning framework for neonatal asphyxia prediction using perinatal clinical features.Frontiers in 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
- Machine learning for preventing stillbirths: is it possible to transform data into life-saving insights?BMC pregnancy and childbirth · 2025Article
- Time series analysis for forecasting neonatal intensive care unit census and neonatal mortality.BMC pediatrics · 2025Article
- Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus.BMC nursing · 2025Article
- Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.Surgical endoscopy · 2025Article
- Development and Validation of Prediction Model for Neonatal Intensive Care Unit (NICU) Admission Using Machine Learning and Multivariate Statistical Approach.Journal of obstetrics and gynaecology of India · 2025Article
- Article
- 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
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Analysis and knowledge extraction of newborn resuscitation activities from annotation files.BMC medical informatics and decision making · 2024Article
- A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data.Children (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPrediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians' ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques.
methodsThis study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes.
results17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models.
conclusionUsing the developed machine learning models can help physicians predict the neonatal deaths in NICUs.
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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.