Primary Care

Latest AI and machine learning research in primary care for healthcare professionals.

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A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

AIMS: To develop a machine learning model for predicting rapid kidney function decline in people wit...

Computational screening of umami tastants using deep learning.

Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often a...

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oe...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascul...

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.

BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of ...

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound i...

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 mA reader protein.

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and ...

Current status and dilemmas of osteoporosis screening tools: A narrative review.

OBJECTIVE: This review aims to explore the strengths and dilemmas of existing osteoporosis screening...

Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.

Based on bioinformatics and machine learning methods, we conducted a study to screen the core genes ...

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obe...

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis ...

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.

AIMS: This study aims to compare the performance of contemporary machine learning models with statis...

The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.

The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol co...

Video-audio neural network ensemble for comprehensive screening of autism spectrum disorder in young children.

A timely diagnosis of autism is paramount to allow early therapeutic intervention in preschoolers. D...

Machine learning and deep learning-based approach to categorize Bengali comments on social networks using fused dataset.

Through the advancement of the contemporary web and the rapid adoption of social media platforms suc...

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