ArticleWorld journal of diabetes2023
Establishment and evaluation of a risk prediction model for gestational diabetes mellitus.
Article in World journal of diabetes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Development and internal validation of a prediction model for healthcare-associated infections in women with gestational diabetes mellitus undergoing delivery.BMC infectious diseases · 2026Article
- A dual track predictive model for assessing crow's feet aging across different clinical severity grades.Scientific reports · 2026Article
- Gestational diabetes mellitus (GDM) in the first-reported pregnancy modifies association between interpregnancy-weight-change and GDM risk in the subsequent pregnancy.AJOG global reports · 2026Article
- Article
- Risk factors for gestational diabetes mellitus and postpartum glucose intolerance: a retrospective study from a hospital specializing in infertility treatment.Scientific reports · 2025Article
- Prediction of Gestational Diabetes Mellitus: A Nomogram Model Incorporating Lifestyle, Nutrition and Health Literacy Factors.Nutrients · 2025Article
- Review
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China.BMC pregnancy and childbirth · 2025Article
- Causal inference and risk prediction of gestational diabetes mellitus based on case-control study and Mendel randomization.Frontiers in nutrition · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- 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
- Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus.Scientific reports · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM.
aimTo develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention.
methodsThe clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified.
resultsPre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (
conclusionThe random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.
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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.