ArticleJournal of endocrinological investigation2024
Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach.
Article in Journal of endocrinological investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 7 citations in OpenAlex.
- Gestational Diabetes Mellitus Across the Perinatal Continuum: A Narrative Review of Woman-Centered, Holistic Care Models.Healthcare (Basel, Switzerland) · 2026Review
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- Early prediction of late-pregnancy hypertriglyceridemia in women with gestational diabetes: development and internal validation of a clinical risk model.Frontiers in endocrinology · 2026Article
- Development and validation of an interpretable machine learning model for predicting incident gestational hypothyroidism using clinical laboratory markers.Frontiers in medicine · 2026Article
- Developing a prediction model of neonatal pneumonia in patients with gestational diabetes mellitus in primary medical institutions.Frontiers in pediatrics · 2026Article
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction.Scientific reports · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles.Digital healthArticle
- A machine learning-based clinical decision support system for effective stratification of gestational diabetes mellitus and management through Ayurveda.Journal of Ayurveda and integrative medicineArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundGestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.
methodsWe conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.
resultsGDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.
conclusionsThe GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .
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