ReviewJournal of diabetes science and technology2015
Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.
Review in Journal of diabetes science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- Metformin Beyond Glycemic Control: Cardiovascular Protection and Diabetes Prevention.Journal of cardiovascular development and disease · 2026Review
- Predictive voting model for early diagnosis of diabetes dataset.Frontiers in cardiovascular medicine · 2026Article
- An Algorithm for Identifying Microvascular Complications in Type 1 Diabetes in the Clinical Setting: Development and External Validation.Health data science · 2026Article
- Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.Annals of translational medicine · 2025Review
- Medication time of metformin and sulfonylureas and incidence of cardiovascular diseases and mortality in type 2 diabetes: a pooled cohort analysis.Scientific reports · 2025Article
- Development and validation of prediction models for stroke and myocardial infarction in type 2 diabetes based on health insurance claims: does machine learning outperform traditional regression approaches?Cardiovascular diabetology · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Guidelines for the Management of Complications of Diabetes in Saudi Arabia Using Delphi Technique for Consensus Among National Experts.Clinics and practice · 2024Article
- Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.Journal of medical Internet research · 2024Article
- Indirect estimation of the prevalence of type 2 diabetes mellitus in the sub-population of Tehran: using non-laboratory risk-score models in Iran.BMC public health · 2024Article
- Enhancing Transparency and Reporting Standards in Diabetes Prediction Modeling: The Significance of the TRIPOD+AI 2024 Statement.Journal of diabetes science and technology · 2024Article
- Machine learning based predictive model of Type 2 diabetes complications using Malaysian National Diabetes Registry: A study protocol.Journal of public health research · 2024Article
- Exploring the Initial Encounter with Continuous Glucose Monitoring among Individuals with Type 2 Diabetes: A Two-Week Trip.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study.JMIR formative research · 2023Article
- Prognostic models for short-term annual risk of severe complications and mortality in patients living with type 2 diabetes using a national medical claim database.Diabetology & metabolic syndrome · 2023Article
- Frameworks for Developing Machine Learning Models.Journal of diabetes science and technology · 2023Article
- Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited.Journal of diabetes science and technology · 2023Article
- Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.Life (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.
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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.