Primary Care

Exercise & Fitness

Latest AI and machine learning research in exercise & fitness for healthcare professionals.

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Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Given the limited capacity to accurately determine the necessity for intubation in intensive care un...

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from n...

Circulating endothelial progenitor cells and inflammatory markers in type 1 diabetes after an acute session of aerobic exercise.

OBJECTIVE: To determine circulating endothelial progenitor cells (EPC) counts and levels of inflamma...

Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models.

Nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal v...

Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.

In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is ...

Review of deep representation learning techniques for brain-computer interfaces.

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniq...

Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe.

Step counting devices were previously shown to be efficient in a variety of applications such as ath...

Intelligent wearable-assisted digital healthcare industry 5.0.

The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearab...

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...

Non-invasive brain-machine interface control with artificial intelligence copilots.

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and c...

Modeling health risks using neural network ensembles.

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

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...

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Many models using the aid of artificial intelligence have been recently proposed to predict the prog...

Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections.

We trained machine learning models to identify IMI in late-lactation cows at dry-off to guide antibi...

Visualization obesity risk prediction system based on machine learning.

Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-e...

Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.

Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking ...

Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. ...

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Physical inactivity is a significant public health concern. Consideration of inter-individual variat...

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