ArticleSensors (Basel, Switzerland)2022
Machine Learning-Based Risk Stratification for Gestational Diabetes Management.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A Framework for Women's Health Engineering Education: Pedagogical Foundations and Practical Implementation.Annals of biomedical engineering · 2026Review
- Unveiling the heterogeneity of T2DM: a narrative review of clustering algorithms in stratifying comorbidity and complication risk.Cardiovascular diabetology. Endocrinology reports · 2026Review
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Review
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Patient-Centred Gestational Diabetes Care: Preference Elicitation Methods and Machine Learning Innovations.Patient preference and adherence · 2025Review
- The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.Clinical diabetes and endocrinology · 2024Review
- Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.IEEE reviews in biomedical engineering · 2024Review
- A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.Sensors (Basel, Switzerland) · 2023Article
- Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.Sensors (Basel, Switzerland) · 2023Article
- Machine learning and disease prediction in obstetrics.Current research in physiology · 2023Review
- 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
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK's National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019-0.023], 0.482 [0.442-0.516], and 0.112 [0.109-0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.