ArticleBMC bioinformatics2020
Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06195566 (Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management), which is not on this map. Cited by 11 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.
Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Machine Learning and Smart Devices for Diabetes Management: Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Special Issue-Diabetes Mellitus: Current Research and Future Perspectives.Journal of personalized medicine · 2024Article
- Toward mechanistic medical digital twins: some use cases in immunology.Frontiers in digital health · 2024Review
- Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges.Journal of medical Internet research · 2023Review
- A psycho-educational intervention programme for parents with SGA foetuses supported by an adaptive mHealth system: design, proof of concept and usability assessment.BMC medical informatics and decision making · 2022Article
- Machine and cognitive intelligence for human health: systematic review.Brain informatics · 2022Review
- Machine learning for diabetes clinical decision support: a review.Advances in computational intelligence · 2022Review
- Toward computational modelling on immune system function.BMC bioinformatics · 2020Article
- Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.BMC bioinformatics · 2020Article
- Article
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.
Funding
Abstract
backgroundThe aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals.
resultsWe analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes.
conclusionsThe resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .
Indexed as
Identifiers
What Socratic 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 Socratic graph.