Exercise & Fitness

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential ...

ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine.

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid...

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the...

Optimizing motor imagery BCI models with hard trials removal and model refinement.

Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensit...

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical pe...

Effect of robot-assisted gait training on improving cardiopulmonary function in stroke patients: a meta-analysis.

OBJECTIVE: Understanding the characteristics related to cardiorespiratory fitness after stroke can provide reference values for patients in clinical r...

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable pheno...

Barriers and facilitators to technology acceptance of socially assistive robots in older adults - A qualitative study based on the capability, opportunity, and motivation behavior model (COM-B) and stakeholder perspectives.

This study aimed to identify barriers and facilitators to older adults' acceptance of socially assistive robots from a stakeholder perspective. We enl...

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. Howe...

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint...

Automated discovery of symbolic laws governing skill acquisition from naturally occurring data.

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under expe...

Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.

Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual f...

Stacked neural network for predicting polygenic risk score.

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results ...

Predictive modelling and identification of key risk factors for stroke using machine learning.

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk ...

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learnin...

Workout Classification Using a Convolutional Neural Network in Ensemble Learning.

To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classi...

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in...

Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement.

Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution b...

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data.

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have p...

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of a...