ArticleScientific reports2020
Nationwide prediction of type 2 diabetes comorbidities.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Pooled it
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review.Cardiovascular diabetology · 2023Pooled it
- Development and External Validation of an Electronic Health Record-Derived Model for 30-Day Periprocedural Ischemic Stroke.Journal of the American Heart Association · 2026Article
- Predicting cardiovascular death in overweight/obese people with prediabetes using machine learning-a proof-of-concept study.Cardiovascular diabetology · 2026Article
- Detecting comorbidity patterns in rare disease patients with machine learning.Frontiers in epidemiology · 2026Article
- Double-Layer Simplified Complex Interval Neural Network Stacking for Blood Glucose Prediction of Continuous Glucose Monitoring System.Biosensors · 2025Article
- Comparing the predictive performance of diabetes complications using administrative health data and clinical data.Scientific reports · 2025Article
- Electronic Health Records: A Gateway to AI-Driven Multimorbidity Solutions-A Comprehensive Systematic Review.Journal of clinical medicine · 2025Review
- Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.BMJ open diabetes research & care · 2024Article
- Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities.Scientific reports · 2023Article
- Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.Sensors (Basel, Switzerland) · 2022Review
- Risk stratification using Artificial Intelligence: Could it be useful to reduce the burden of chronic kidney disease in low- and middle-income Countries?Frontiers in public health · 2022Article
- Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death.Endocrinology, diabetes & metabolism · 2021Article
- Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.JAMA network open · 2021Article
- Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.NPJ digital medicine · 2021Article
- An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.Frontiers in artificial intelligence · 2021Article
- Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research.Diabetologia · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.
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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.