ArticleJournal of Korean medical science2019
Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants.
Article in Journal of Korean medical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.Acta obstetricia et gynecologica Scandinavica · 2023Pooled it
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- 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
- Machine-Learning-Based Prediction of Preterm Birth in Women with Huge Uterine Fibroids: A Stratified Cohort Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability.Obstetrics & gynecology science · 2026Review
- 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 prediction models for mode of delivery in prolonged pregnancies in Sweden.Scientific reports · 2025Article
- Predicting preterm birth using electronic medical records from multiple prenatal visits.BMC pregnancy and childbirth · 2024Article
- Clinical and dental predictors of preterm birth using machine learning methods: the MOHEPI study.Scientific reports · 2024Article
- A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests.Journal of biomedical physics & engineering · 2024Article
- Deep Learning in Predicting Preterm Birth: A Comparative Study of Machine Learning Algorithms.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2024Article
- Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.BMC pregnancy and childbirth · 2024Article
- Exploring the potential of machine learning in gynecological care: a review.Archives of gynecology and obstetrics · 2024Review
- Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.Turkish journal of medical sciences · 2024Article
- Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.Scientific reports · 2023Article
- A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth.International journal of molecular sciences · 2023Article
- Multiomic Investigations into Lung Health and Disease.Microorganisms · 2023Review
- Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.PloS one · 2023Article
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLittle research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.
methodsData came from Anam Hospital in Seoul, Korea, with 596 obstetric patients during March 27, 2014 - August 21, 2018. Six machine learning methods were applied and compared for the prediction of preterm birth. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of preterm birth. Analysis was done in December, 2018.
resultsThe accuracy of the ANN (0.9115) was similar with those of logistic regression and the random forest (0.9180 and 0.8918, respectively). Based on variable importance from the ANN, major determinants of preterm birth are body mass index (0.0164), hypertension (0.0131) and diabetes mellitus (0.0099) as well as prior cone biopsy (0.0099), prior placenta previa (0.0099), parity (0.0033), cervical length (0.0001), age (0.0001), prior preterm birth (0.0001) and myomas & adenomyosis (0.0001).
conclusionFor preventing preterm birth, preventive measures for hypertension and diabetes mellitus are required alongside the promotion of cervical-length screening with different guidelines across the scope/type of prior conization.
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