AIMC Topic: Glycated Hemoglobin
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Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.
Frontiers in endocrinology
Jan 20, 2023
OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurem...
Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes.
Nature communications
Nov 14, 2022
Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of the total healthcare spending in the United States (US). Current treatment guidelines endorse a massive number of potential anti-...
Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.
Journal of biomedical informatics
Oct 8, 2022
Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) ...
Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.
Diabetes research and clinical practice
Jul 6, 2022
AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.
Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.
Nutrients
Jan 15, 2022
The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management...
Machine Learning
Biomarkers
Prognosis
Retrospective Studies
Humans
Artificial Intelligence
Male
Middle Aged
Insulin-Like Growth Factor I
Health Status
Female
Risk Factors
Adult
Obesity, Metabolically Benign
Glycated Hemoglobin
Alanine Transaminase
Absorptiometry, Photon
Metabolic Syndrome
Obesity
Adipose Tissue
90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c.
Sensors (Basel, Switzerland)
Nov 24, 2021
Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the pre...
Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.
Annals of epidemiology
Jul 17, 2021
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal fore...
An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from , Decreases HbA1c in a Prediabetic Population.
Nutrients
May 13, 2021
The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose l...
Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.
BMC endocrine disorders
May 4, 2021
INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and v...
Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patie...
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