ReviewIEEE reviews in biomedical engineering2024
Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.
Review in IEEE reviews in biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence in Self-Management of Gestational Diabetes Mellitus: A Systematic Review.Journal of medical systems · 2026Pooled it
- Quality and Multifunctionality in Mobile Apps for Gestational Diabetes: Systematic App Review.JMIR mHealth and uHealth · 2026Pooled it
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Digital versus non-digital health interventions to improve iron supplementation in pregnant women: a systematic review and meta-analysis.Frontiers in medicine · 2024Pooled it
- Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.Scientific reports · 2026Article
- Enhancing Early Prediction of Gestational Diabetes Mellitus Through Data Augmentation and Feature Guidance: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Cost-consequence analysis of a digital health-enabled non-communicable disease management intervention in Ghana.Cost effectiveness and resource allocation : C/E · 2026Article
- GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept.JMIR diabetes · 2026Article
- Machine learning and engagement insights for personalized blood glucose management.Frontiers in digital health · 2026Article
- Management of cardiometabolic risk factors in cardiovascular high-risk populations with varying cognitive levels.Aging clinical and experimental research · 2025Article
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Understanding gestational diabetes in Hong Kong: women's needs, self-management challenges, and the potential of digital health solutions in culturally sensitive care.BMC pregnancy and childbirth · 2025Article
- The Role of Probiotics in Preventing Gestational Diabetes: An Umbrella Review.Journal of clinical medicine · 2025Review
- Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.PeerJ. Computer science · 2025Article
- Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort.Frontiers in medicine · 2025Article
- Acceptance of the Istel Care Telehealth System by Women with Gestational Diabetes (GDM) in Routine Care in Poland.Patient preference and adherence · 2025Article
- Article
- A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.Sensors (Basel, Switzerland) · 2023Article
- Trial protocol for the study of recommendation system DiaCompanion with personalized dietary recommendations for women with gestational diabetes mellitus (DiaCompanion I).Frontiers in endocrinology · 2023Article
- Hybrid deep learning for IoT-based health monitoring with physiological event extraction.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.
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.