ArticleComputers in biology and medicine2018
Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes.
Article in Computers in biology and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 36 citations in OpenAlex.
- Shared mechanisms of musculoskeletal dysfunction in type 2 diabetes mellitus: Insights into future therapeutic directions (Review).International journal of molecular medicine · 2026Review
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Applications of AI in Predicting Drug Responses for Type 2 Diabetes.JMIR diabetes · 2025Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.International journal of molecular sciences · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Greater Glycemic Burden Is Associated with Further Poorer Glycemic Control in Newly-Diagnosed Type 2 Diabetes Mellitus Patients.Nutrients · 2022Observational
- Article
- Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.The AAPS journal · 2021Article
- Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes.Frontiers in pharmacology · 2021Article
- Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.Current diabetes reports · 2020Review
- Does the Encounter Type Matter When Defining Diabetes Complications in Electronic Health Records?Medical care · 2020Article
- Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?Digital healthReview
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveMetformin is the preferred first-line medication for management of type 2 diabetes and prediabetes. However, over a third of patients experience primary or secondary therapeutic failure. We developed machine learning models to predict which patients initially prescribed metformin will achieve and maintain control of their blood glucose after one year of therapy. MATERIALS AND
methodsWe performed a retrospective analysis of administrative claims data for 12,147 commercially-insured adults and Medicare Advantage beneficiaries with prediabetes or diabetes. Several machine learning models were trained using variables available at the time of metformin initiation to predict achievement and maintenance of hemoglobin A
resultsAUC performances based on five-fold cross-validation ranged from 0.58 to 0.75. The most influential variables driving the predictions were baseline HbA
conclusionsMachine learning models can effectively predict primary or secondary metformin treatment failure within one year. This information can help identify effective individualized treatment strategies. Most of the implemented models outperformed traditional logistic regression, highlighting the potential for applying machine learning to problems in medicine.
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