ArticleJournal of healthcare engineering2022
Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 38 citations in OpenAlex.
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Comparative machine learning models for early prediction of preterm birth from maternal serum biomarkers.BMC pregnancy and childbirth · 2026Article
- Cost-Effectiveness of Fetal Fibronectin Testing in Women at Risk for Preterm Birth: A Retrospective Cohort Study.ClinicoEconomics and outcomes research : CEOR · 2026Article
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- Machine learning-based prediction algorithm of spontaneous preterm birth using multi-source data.BMC pregnancy and childbirth · 2025Article
- Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.Scientific reports · 2025Article
- Review
- Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland.Healthcare (Basel, Switzerland) · 2025Article
- A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia.International journal of general medicine · 2025Article
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.BMC pregnancy and childbirth · 2024Article
- Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling.BMC pregnancy and childbirth · 2024Article
- Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.Scientific reports · 2024Article
- Gestational diabetes mellitus aggravates adverse perinatal outcomes in women with intrahepatic cholestasis of pregnancy.Diabetology & metabolic syndrome · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.Scientific reports · 2023Article
- A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology.Cureus · 2023Review
- Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.PloS one · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Preterm birth (PTB) was one of the leading causes of neonatal death. Predicting PTB in the first trimester and second trimester will help improve pregnancy outcomes. The aim of this study is to propose a prediction model based on machine learning algorithms for PTB. Method: Data for this study were reviewed from 2008 to 2018, and all the participants included were selected from a hospital in China. Six algorisms, including Naive Bayesian (NBM), support vector machine (SVM), random forest tree (RF), artificial neural networks (ANN), K-means, and logistic regression, were used to predict PTB. The receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess the performance of the model. Results: A total of 9550 pregnant women were included in the study, of which 4775 women had PTB. A total of 4775 people were randomly selected as controls. Based on 27 weeks of gestation, the area under the curve (AUC) and the accuracy of the RF model were the highest compared with other algorithms (accuracy: 0.816; AUC = 0.885, 95% confidence interval (CI): 0.873-0.897). Meanwhile, there was positive association between the accuracy and AUC of the RF model and gestational age. Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin were the main influence factors of PTB. Conclusion: The results indicated that the prediction model based on the RF algorithm had a potential value to predict preterm birth in the early stage of pregnancy. The important analysis of the RF model suggested that intervention for main factors of PTB in the early stages of pregnancy would reduce the risk of PTB.
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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.