ReviewEuropean journal of clinical investigation2023
Big data and machine learning to tackle diabetes management.
Review in European journal of clinical investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 29 citations in OpenAlex.
- Predictive voting model for early diagnosis of diabetes dataset.Frontiers in cardiovascular medicine · 2026Article
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- Clinical application of cluster analysis in patients with newly diagnosed type 2 diabetes.Hormones (Athens, Greece) · 2025Article
- Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.PeerJ. Computer science · 2025Article
- Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.PloS one · 2025Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Personalized and predictive strategies for diabetic foot ulcer prevention and therapeutic management: potential improvements through introducing Artificial Intelligence and wearable technology.Medicine and pharmacy reports · 2024Review
- Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning.JMIR formative research · 2024Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
- Machine learning prediction of mortality in Acute Myocardial Infarction.BMC medical informatics and decision making · 2023Article
- Editorial: Metabolic miscommunication among organs: The missing links.Frontiers in endocrinology · 2023Article
- Big data and machine learning to tackle diabetes management.European journal of clinical investigation · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundType 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.
methodsIn this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D.
resultsTo correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology.
conclusionFully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.
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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.