AIMC Topic: Glucose Tolerance Test
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Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men.
Frontiers in endocrinology
Apr 24, 2025
BACKGROUND: Isolated Impaired Glucose Tolerance (I-IGT) represents a specific prediabetic state that typically requires a standardized oral glucose tolerance test (OGTT) for diagnosis. This study aims to predict glucose tolerance status in Chinese Ha...
Early gestational diabetes mellitus risk predictor using neural network with NearMiss.
Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology
Feb 24, 2025
BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify ...
Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.
Translational psychiatry
Feb 18, 2025
Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated m...
StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study protocol.
BMJ open
Dec 26, 2024
INTRODUCTION: The incidence of gestational diabetes mellitus (GDM) in Australia has tripled in the last 20 years. Consequently, over 40 000 pregnancies are now diagnosed as 'higher risk' each year. This has increased antenatal surveillance and obstet...
Pregnancy Outcome
Logistic Models
Observational Studies as Topic
Diabetes, Gestational
Pregnancy
Female
Risk Assessment
Incidence
Multilevel Analysis
New South Wales
Hospitals, Public
Routinely Collected Health Data
Predictive Learning Models
Glucose Tolerance Test
Information Storage and Retrieval
Propensity Score
Delivery, Obstetric
Humans
Research Design
Retrospective Studies
Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.
Computer methods and programs in biomedicine
Oct 31, 2024
BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study...
The early prediction of gestational diabetes mellitus by machine learning models.
BMC pregnancy and childbirth
Aug 31, 2024
BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.
Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.
Journal of diabetes investigation
Mar 4, 2024
AIMS/INTRODUCTION: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed ...
DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.
Scientific reports
Jan 18, 2024
Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and acc...
Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.
Frontiers in endocrinology
Jul 14, 2021
BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise ...
Early Predictors of Gestational Diabetes Mellitus in IVF-Conceived Pregnancies.
Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
Dec 17, 2020
OBJECTIVE: Gestational diabetes mellitus (GDM) is associated with adverse maternal and fetal outcomes. This study aimed to identify early and reliable GDM predictors that would enable implementation of preventive and management measures.
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