Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Showing 1744-1764 of 2,618 articles
Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clin...

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biol...

Thread/paper- and paper-based microfluidic devices for glucose assays employing artificial neural networks.

This paper describes the fabrication of and data collection from two microfluidic devices: a microfl...

Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort.

MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular d...

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitor...

The Problems of Realism-Based Ontology Design: a Case Study in Creating Definitions for an Application Ontology for Diabetes Camps.

A requirement of realism-based ontology design is that classes denote exclusively entities that exis...

Extracting Healthcare Quality Information from Unstructured Data.

Healthcare quality research is a fundamental task that involves assessing treatment patterns and mea...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

De-identification of clinical notes is a special case of named entity recognition. Supervised machin...

Long-term outcome in inherited nephrogenic diabetes insipidus.

BACKGROUND: Inherited nephrogenic diabetes insipidus (NDI) is a rare disorder characterized by impai...

Antiproliferative and anti-apoptotic effect of astaxanthin in an oxygen-induced retinopathy mouse model.

OBJECTIVE: To evaluate the impact of intravitreal (IV) and intraperitoneal (IP) astaxanthin (AST) in...

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8...

Impaired left ventricular diastolic function in T2DM patients is closely related to glycemic control.

BACKGROUND: Left ventricular (LV) diastolic dysfunction commonly is observed in individuals with typ...

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation.

The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms...

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure...

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem invo...

The Promise and Perils of Wearable Physiological Sensors for Diabetes Management.

Development of truly useful wearable physiologic monitoring devices for use in diabetes management i...

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.

PURPOSE: Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individua...

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors.

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabet...

Antiphospholipase 2 receptor antibody levels to predict complete spontaneous remission in primary membranous nephropathy.

BACKGROUND: M-type phospholipase A2 receptor (APLA2R) is considered the major antigen involved in th...

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PURPOSE: To assess the performance of a deep learning algorithm for detecting referable glaucomatous...

A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies...

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