ArticleArchives of gynecology and obstetrics2024
Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms.
Article in Archives of gynecology and obstetrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Development and internal validation of a prediction model for healthcare-associated infections in women with gestational diabetes mellitus undergoing delivery.BMC infectious diseases · 2026Article
- Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.Scientific reports · 2026Article
- 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
- Machine Learning-Based Early Prediction of Gestational Diabetes Using First-Trimester Laboratory Parameters.Cureus · 2026Article
- Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients.BioData mining · 2026Article
- Development and external validation of a non-invasive early gestational diabetes mellitus prediction model integrating social network variables: a machine learning-based prospective cohort study.BMC pregnancy and childbirth · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 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
- Evaluation of machine learning models for early prediction of gestational diabetes using retrospective electronic health records from current and previous pregnancies.BMJ digital health & AI · 2025Article
- Prediction of dialysis adequacy using data-driven machine learning algorithms.Renal failure · 2024Article
- The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.Clinical diabetes and endocrinology · 2024Review
- Untargeted metabolomics profiling of gestational diabetes mellitus: insights into early diagnosis and metabolic pathway alterations.Frontiers in molecular biosciences · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeShort- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated co-morbidities. The aim is to use machine learning (ML) algorithms to study data gathered throughout the first trimester in order to predict GDM.
methodsTwo independent cohorts with forty-five features gathered through first trimester were included. We constructed prediction models based on three different algorithms and traditional logistic regression, and deployed additional two ensemble algorithms to identify the importance of individual features.
results4799 and 2795 pregnancies were included in the Xinhua Hospital Chongming branch (XHCM) and the Shanghai Pudong New Area People's Hospital (SPNPH) cohorts, respectively. Extreme gradient boosting (XGBoost) predicted GDM with moderate performance (the area under the receiver operating curve (AUC) = 0.75) at pregnancy initiation and good-to-excellent performance (AUC = 0.99) at the end of the first trimester in the XHCM cohort. The trained XGBoost showed moderate performance in the SPNPH cohort (AUC = 0.83). The top predictive features for GDM diagnosis were pre-pregnancy BMI and maternal abdominal circumference at pregnancy initiation, and FPG and HbA1c at the end of the first trimester.
conclusionOur work demonstrated that ML models based on the data gathered throughout the first trimester achieved moderate performance in the external validation cohort.
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