AIMC Topic: Diabetes Mellitus
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An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.
BMC medical research methodology
Sep 27, 2024
Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective w...
Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor.
Computers in biology and medicine
Sep 25, 2024
For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at least three times daily, amounting to an average of six meals. Unfortunately, many individuals tend...
Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.
JMIR research protocols
Sep 25, 2024
BACKGROUND: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.
Deep learning for blood glucose level prediction: How well do models generalize across different data sets?
PloS one
Sep 25, 2024
Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the...
Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.
Expert review of pharmacoeconomics & outcomes research
Sep 23, 2024
OBJECTIVES: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.
Diabetes Mellitus
Age Factors
Cluster Analysis
Unsupervised Machine Learning
Surveys and Questionnaires
United States
Hypoglycemic Agents
Pharmacists
Professional Role
Pharmaceutical Services
Insurance, Health
Self Report
Sex Factors
Diabetes Complications
Humans
Machine Learning
Female
Aged
Adult
Male
Middle Aged
Exploration of fractional flow reservation score based on artificial intelligence post-processing for coronary artery lesions in patients with diabetes and coronary heart disease.
SLAS technology
Sep 21, 2024
In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reservation (FFR) in patients with different levels of CHD and diabetes, this paper used AI (artificial intelligence) post-processing technology to detect ...
Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sep 20, 2024
Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is develope...
Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.
eLife
Sep 20, 2024
BACKGROUND: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the ...
Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.
Applied spectroscopy
Sep 5, 2024
Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus thro...
Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.
Computer methods and programs in biomedicine
Aug 23, 2024
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...
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