AIMC Topic: Diabetes Mellitus, Type 2
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Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.
IEEE journal of biomedical and health informatics
Feb 6, 2015
The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The ai...
The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.
Medical decision making : an international journal of the Society for Medical Decision Making
Dec 1, 2014
OBJECTIVE: To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort ...
Hypoglycemia prediction using machine learning models for patients with type 2 diabetes.
Journal of diabetes science and technology
Oct 14, 2014
Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine lea...
Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.
Chinese medical journal
Aug 20, 2025
BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical found...
Type 2 Diabetes Subtyping via Phenotype and Genotype Co-Learning.
Studies in health technology and informatics
Aug 7, 2025
Interpreting and subtyping type 2 diabetes (T2D) is challenging yet essential for achieving fine-grained pathophysiological insights and precise clinical stratification. Previous studies have primarily relied on a small number of pre-selected risk fa...
Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.
Computer methods and programs in biomedicine
Aug 1, 2025
BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, w...
The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.
Computer methods and programs in biomedicine
Aug 1, 2025
BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a co...
Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.
Diabetes, obesity & metabolism
Aug 1, 2025
AIMS: A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and cardiometabolic outcomes. Considering five major T2D drug classes, we aimed to describe variatio...
Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials.
Primary care diabetes
Aug 1, 2025
This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in improving glycemic control, measured by Time in Range (TIR) and HbA1c levels, in patients with diabetes. Included studies were randomized controlled tria...
Generative AI vs. human expertise: a comparative analysis of case-based rational pharmacotherapy question generation.
European journal of clinical pharmacology
Jun 1, 2025
PURPOSE: This study evaluated the performance of three generative AI models-ChatGPT- 4o, Gemini 1.5 Advanced Pro, and Claude 3.5 Sonnet-in producing case-based rational pharmacology questions compared to expert educators.
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