ArticleScientific reports2023
Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children's Study.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- Maternal serum NRF2 at 12 weeks as a biomarker for development of gestation diabetes mellitus.Archives of gynecology and obstetrics · 2026Article
- Review
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery.Cureus · 2025Review
- Advanced Machine Learning did not Surpass Traditional Logistic Regression in First-Trimester Gestational Diabetes Mellitus Prediction: A Retrospective Single-Center Study From Eastern China.International journal of general medicine · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. Exploratory analysis of the JECS data revealed that health-related quality of life in early pregnancy and maternal birthweight, which were rarely reported to be associated with GDM, were found along with variables that were reported to be associated with GDM. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data.
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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 Socratic graph.