AIMC Topic: Diabetes Mellitus, Type 2
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A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.
Journal of endocrinological investigation
Dec 31, 2023
OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of h...
Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations.
Journal of biomolecular structure & dynamics
Dec 15, 2023
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse sid...
Can ChatGPT provide appropriate meal plans for NCD patients?
Nutrition (Burbank, Los Angeles County, Calif.)
Nov 11, 2023
OBJECTIVES: Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various...
Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach.
Computer methods in biomechanics and biomedical engineering
Oct 22, 2023
Diabetes mellitus is a severe condition that has the potential to impair strength. The disease known as diabetes mellitus, which is a chronic condition, is brought on by a significant rise in blood glucose levels. The diagnosis of this condition is m...
Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases.
Kidney international
Sep 27, 2023
Podocyte injury plays a key role in pathogenesis of many kidney diseases with increased podocyte foot process width (FPW), an important measure of podocyte injury. Unfortunately, there is no consensus on the best way to estimate FPW and unbiased ster...
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.
Proceedings of the National Academy of Sciences of the United States of America
Aug 21, 2023
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal rema...
Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.
Nature communications
Jul 7, 2023
Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by ...
Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model.
ChemistryOpen
Jul 5, 2023
Recent studies have identified G protein-coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and ...
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.
International journal of medical informatics
Apr 18, 2023
AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).
A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes.
Physical and engineering sciences in medicine
Apr 11, 2023
Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors related to diabetic retinopathy (DR). However, the problem of visualizing CGM data and automatically predicting the incidence of DR from CGM is still ...
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