ArticleJournal of diabetes science and technology2015
Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.
Article 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 52 papers, 4 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled 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
- Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis.Journal of diabetes investigation · 2022Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator.The Journal of allergy and clinical immunology · 2018Trial
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention.Frontiers in endocrinology · 2026Review
- Research on a Predictive Model for Microalbuminuria in Type 2 Diabetes Based on Machine Learning and SHAP Analysis.International journal of endocrinology · 2026Article
- Predicting and classifying type 2 diabetes using a transparent ensemble model combining random forest, k-nearest neighbor, and neural networks.Scientific reports · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- A Comprehensive Investigation: Developing the Pharmaceutical Industry through Artificial Intelligence.Current drug discovery technologies · 2025Review
- The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.Frontiers in public health · 2025Article
- Development and validation of a chronic kidney disease progression model using patient-level simulations.Renal failure · 2024Article
- Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.Scientific reports · 2024Article
- Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.BMC medical informatics and decision making · 2024Article
- PyCaret for Predicting Type 2 Diabetes: A Phenotype- and Gender-Based Approach with the "Nurses' Health Study" and the "Health Professionals' Follow-Up Study" Datasets.Journal of personalized medicine · 2024Article
- Evaluating ChatGPT's Utility in Medicine Guidelines Through Web Search Analysis.The Permanente journal · 2024Article
- Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.PloS one · 2024Article
- Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective.Journal of diabetes research · 2023Article
- Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.Journal of multidisciplinary healthcare · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApplication of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D).
methodsUsing a novel analytical platform (Reverse Engineering and Forward Simulation [REFS]), we built prediction model ensembles for progression to prediabetes or T2D from an aggregated EHR data sample. REFS relies on a Bayesian scoring algorithm to explore a wide model space, and outputs a distribution of risk estimates from an ensemble of prediction models. We retrospectively followed 24 331 adults for transitions to prediabetes or T2D, 2007-2012. Accuracy of prediction models was assessed using an area under the curve (AUC) statistic, and validated in an independent data set.
resultsOur primary ensemble of models accurately predicted progression to T2D (AUC = 0.76), and was validated out of sample (AUC = 0.78). Models of progression to T2D consisted primarily of established risk factors (blood glucose, blood pressure, triglycerides, hypertension, lipid disorders, socioeconomic factors), whereas models of progression to prediabetes included novel factors (high-density lipoprotein, alanine aminotransferase, C-reactive protein, body temperature; AUC = 0.70).
conclusionsWe constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research.
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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.