AIMC Topic: Glycated Hemoglobin

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Predicting Diabetic Neuropathy Risk Level Using Artificial Neural Network and Clinical Parameters of Subjects With Diabetes.

Journal of diabetes science and technology Oct 22, 2020
BACKGROUND: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration percept...
Glycated Hemoglobin Sensory Thresholds Diabetic Neuropathies Vibration Humans Neural Networks, Computer Diabetes Mellitus, Type 2
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A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Nature biomedical engineering Oct 12, 2020
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using divers...
Risk Assessment Deep Learning Body Mass Index Blood Pressure Glycated Hemoglobin Datasets as Topic Aged, 80 and over Humans Coronary Disease Adult Cholesterol Retrospective Studies Photography Male Stroke Female Retinal Vessels Middle Aged Myocardial Infarction Risk Factors Hypertensive Retinopathy Retina Aged Image Interpretation, Computer-Assisted
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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.

Journal of diabetes research Jun 12, 2020
BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible predi...
Decision Trees Glycated Hemoglobin Cholesterol Uric Acid Diabetes, Gestational Adult Blood Glucose Cohort Studies Logistic Models Routinely Collected Health Data Lipoproteins, HDL Maternal Age Triglycerides Clinical Decision Rules Pregnancy in Obesity Humans Machine Learning Retrospective Studies Pregnancy Female Reproducibility of Results Young Adult
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A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

IEEE journal of biomedical and health informatics Jan 17, 2020
[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of dail...
Humans Female Aged Adult Diabetes Mellitus, Type 1 Male Glycated Hemoglobin Middle Aged Diagnosis, Computer-Assisted Deep Learning
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Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning.

Pharmacology research & perspectives Nov 20, 2019
Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes whi...
Adrenergic alpha-1 Receptor Antagonists Machine Learning Electronic Health Records Male Big Data Humans Diabetes Mellitus, Type 2 Blood Glucose Prostatic Hyperplasia Drug Repositioning Glycated Hemoglobin Data Mining
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EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research.

BMC medical informatics and decision making Nov 15, 2019
BACKGROUND: Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this da...
Information Storage and Retrieval Vital Signs Sensitivity and Specificity Stroke Volume Natural Language Processing Glycated Hemoglobin Creatinine Humans Algorithms Electronic Health Records
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Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.

Diabetes, obesity & metabolism Sep 30, 2019
AIM: To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes me...
Humans Machine Learning Algorithms Insulin Female Diabetes Mellitus, Type 2 Aged Hypoglycemic Agents Male Glycated Hemoglobin Middle Aged
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Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

JAMA network open Sep 4, 2019
IMPORTANCE: Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but...
Hemoglobins Troponin I L-Lactate Dehydrogenase Blood Urea Nitrogen Leukocyte Count Humans Clinical Laboratory Techniques Machine Learning Retrospective Studies Female Middle Aged Hospitalization Aged ROC Curve Adult Predictive Value of Tests Sensitivity and Specificity Area Under Curve Male Glycated Hemoglobin
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Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Cardiovascular diabetology Jun 11, 2019
BACKGROUND: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary dia...
Support Vector Machine Prognosis CpG Islands DNA Methylation Genetic Predisposition to Disease Systems Integration Middle Aged Female Humans Mitochondria, Heart Polymorphism, Single Nucleotide Glycated Hemoglobin Risk Factors Risk Assessment Diabetic Cardiomyopathies Epigenesis, Genetic Disease Progression Genetic Markers Male Models, Genetic Diabetes Mellitus, Type 2 DNA, Mitochondrial Genomics
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Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing ...
Diabetes Mellitus, Type 2 Humans Glycated Hemoglobin Software Knowledge Bases Electronic Health Records Proof of Concept Study Decision Support Systems, Clinical
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