ArticleBMC pregnancy and childbirth2023
Establishment of a model for predicting preterm birth based on the machine learning algorithm.
Article in BMC pregnancy and childbirth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Machine-Learning-Based Prediction of Preterm Birth in Women with Huge Uterine Fibroids: A Stratified Cohort Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Regression-based model for predicting preterm birth using vaginal lactobacilli and routine clinical data.BMC pregnancy and childbirth · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Prediction of preterm and low birth weight risk using a physiology based artificial neural network integrating hematological, dental, and periodontal index markers: a cross sectional study based on machine learning.BMC pregnancy and childbirth · 2026Article
- Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability.Obstetrics & gynecology science · 2026Review
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Predicting the risk of preterm birth with machine learning and electronic health records in China.BMC medical informatics and decision making · 2025Article
- Prediction of spontaneous preterm birth in pregnant women using machine learning.Archives of gynecology and obstetrics · 2025Article
- Review
- Developing a prognostic model for predicting preterm birth using a machine learning algorithm.BMC pregnancy and childbirth · 2025Article
- Perspective on the nursing management for gestational diabetes mellitus: A perspective.Medicine · 2025Review
- Predicting preterm birth using machine learning methods.Scientific reports · 2025Article
- Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.PloS one · 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
- Building a machine learning-based risk prediction model for second-trimester miscarriage.BMC pregnancy and childbirth · 2024Article
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data.Frontiers in pediatrics · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.
methodsThis was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.
resultsA total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%.
conclusionsBy constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.
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