Prevention

[Medicine of the future: the role of artificial intelligence in optimizing nutrition for the health of the Russian population].

One of the most pressing medical, social and government tasks is to ensure health saving, improve the quality of life and increase the life expectancy of the Russian population. The most important lever to ensure the solution of these issues is the optimization of nutrition and the use of methods of artificial intelligence (AI) and new information technologies to support decision-making in this ar...

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real ti...

Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet.

INTRODUCTION: The USA has the highest age-standardized prevalence of inflammatory bowel disease (IBD). Both genetic and environmental factors have bee...

Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine lea...

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone ...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent perform...

Predicting long-term sleep deprivation using wearable sensors and health surveys.

Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognit...

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review.

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutioni...

Temporal Convolutional Neural Network-Based Prediction of Vascular Health in Elderly Women Using Photoplethysmography-Derived Pulse Wave during Exercise.

(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Tempo...

Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.

(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the level...

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values....

AI nutrition recommendation using a deep generative model and ChatGPT.

In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition i...

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.

Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce productivity. Exercise therapy is the gold standard tre...

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture.

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, a...

A machine learning (ML) approach to understanding participation in government nutrition programs.

Machine Learning (ML) affords researchers tools to advance beyond research methods commonly employed in psychology, business, and public policy studie...

Clinicians and AI use: where is the professional guidance?

With the introduction of artificial intelligence (AI) to healthcare, there is also a need for professional guidance to support its use. New (2022) rep...

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluatin...

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrho...

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.

Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a f...

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.

INTRODUCTION: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present,...