Primary Care

Exercise & Fitness

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

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Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network.

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals...

Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening.

Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditi...

A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs.

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditi...

RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG.

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applicati...

Fully interpretable deep learning model of transcriptional control.

MOTIVATION: The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motiva...

Design and Validation of a Lower-Limb Haptic Rehabilitation Robot.

Present robots for investigating lower-limb motor control and rehabilitation focus on gait training....

PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning.

MOTIVATION: Peptide is a promising candidate for therapeutic and diagnostic development due to its g...

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.

MOTIVATION: Therapeutic peptides failing at clinical trials could be attributed to their toxicity pr...

Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.

In this review article, the current and future impact of artificial intelligence (AI) technologies o...

Polar labeling: silver standard algorithm for training disease classifiers.

MOTIVATION: Expert-labeled data are essential to train phenotyping algorithms for cohort identificat...

Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

INTRODUCTION: The World Health Organization has estimated that 12 million deaths occur worldwide, ev...

DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules.

MOTIVATION: Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerat...

[Role of artificial intelligence in the diagnosis and treatment of gastrointestinal diseases].

The rapid development of computer technologies brings us great changes in daily life and work. Artif...

Artificial intelligence-based multi-objective optimization protocol for protein structure refinement.

MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Exist...

Efficacy of an Automated Robotic Cleaning Device for Compounding Pharmacies.

Compounded medicinal products should be prepared using an appropriate quality-assurance system. Clea...

Feasibility of supplemental robot-assisted knee flexion exercise following total knee arthroplasty.

BACKGROUND: The Hybrid Assistive Limb (HAL) is a robotic exoskeleton designed to support impaired li...

PycnoRacer®, a fitness drink including Pycnogenol®, improves recovery and training in the Cooper test.

BACKGROUND: This study evaluates the effects of training (on running distance measured with a Cooper...

20th Annual Feigenbaum Lecture: Echocardiography for Precision Medicine-Digital Biopsy to Deconstruct Biology.

Heart failure with preserved ejection fraction (HFpEF) is a complex, heterogeneous syndrome in need ...

Develop machine learning-based regression predictive models for engineering protein solubility.

MOTIVATION: Protein activity is a significant characteristic for recombinant proteins which can be u...

Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data.

MOTIVATION: Studies have shown that the accuracy of random forest (RF)-based scoring functions (SFs)...

Sarcopenia feature selection and risk prediction using machine learning: A cross-sectional study.

The purpose of this study was to verify the usefulness of machine learning (ML) for selection of ris...

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