Prevention

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few...

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and September 2021 were enrolled. According to the preoperative anesthesia management methodology, patients were rolled into a control (Ctrl) group (using 10 mg...

Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

BACKGROUND: The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use ...

[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...

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 hea...

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...