AIMC Topic: Diabetes Mellitus, Type 2
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Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.
The Journal of clinical endocrinology and metabolism
Jul 12, 2024
CONTEXT: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.
Geno-GCN: A Genome-specific Graph Convolutional Network for Diabetes Prediction.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Drawing inspiration from convolutional neural networks, graph convolutional networks (GCNs) have been implemented in various applications. Yet, the integration of GCNs into clinical settings, particularly in the context of complex health conditions l...
Identifying Prediabetes in Canadian Populations Using Machine Learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among...
Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?
Journal of insurance medicine (New York, N.Y.)
Jul 1, 2024
Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus ...
Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.
Diabetes/metabolism research and reviews
Jul 1, 2024
INTRODUCTION: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH),...
Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study.
The Journal of international medical research
Jun 1, 2024
OBJECTIVE: To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual health checkup records.
An interpretable predictive deep learning platform for pediatric metabolic diseases.
Journal of the American Medical Informatics Association : JAMIA
May 20, 2024
OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the de...
A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial.
Association between serum Galectin-3 and periodontitis in patients with type 2 diabetes mellitus.
Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology
Dec 1, 2023
OBJECTIVES: This study aims to investigate the correlation between serum Galectin-3 levels and the risk of periodontitis in patients with type 2 diabetes mellitus (T2DM).
Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.
Journal of the American Medical Informatics Association : JAMIA
Nov 17, 2023
OBJECTIVE: To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations.
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