ArticleNPJ digital medicine2021
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05144672 (Irisin Level in Patients With Diabetic Nephropathy), which is not on this map. Cited by 51 papers, 3 of them syntheses that pooled it.
What it found
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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.
Irisin Level in Patients With Diabetic Nephropathy
Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.PloS one · 2024Pooled it
- Pooled it
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Anticipating health and care trajectories from routinely collected social care records.npj health systems · 2026Article
- Identification of Patient Clusters with Distinct Disease Progression Patterns Utilizing a Nationwide Finnish Population with Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Article
- Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.JMIR formative research · 2026Article
- An artificial intelligence prediction model for optimizing patient selection for cardiac imaging for the investigation of suspected coronary artery disease.European heart journal. Digital health · 2026Article
- Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Comorbidities, Utilization, and Quality of Care as Predictors of Diabetes Complications.Journal of the American Board of Family Medicine : JABFM · 2026Article
- Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease.Communications medicine · 2025Article
- Principles and Practices of Community Engagement in AI for Population Health: Formative Qualitative Study of the AI for Diabetes Prediction and Prevention Project.Journal of participatory medicine · 2025Article
- Comparing the predictive performance of diabetes complications using administrative health data and clinical data.Scientific reports · 2025Article
- Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes.Nature medicine · 2025Article
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- How machine learning on real world clinical data improves adverse event recording for endoscopy.NPJ digital medicine · 2025Article
- Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.Scientific reports · 2025Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7-77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.
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.