AIMC Topic: Weight Loss
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Uncovering key factors in weight loss effectiveness through machine learning.
International journal of obesity (2005)
May 6, 2025
BACKGROUND/OBJECTIVES: One of the main challenges in weight loss is the dramatic interindividual variability in response to treatment. We aim to systematically identify factors relevant to weight loss effectiveness using machine learning (ML).
Predicting Weight Loss Success After Gastric Sleeve Surgery: A Machine Learning-Based Approach.
Nutrients
Apr 21, 2025
BACKGROUND/OBJECTIVES: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of too...
AI-Powered Analysis of Weight Loss Reports from Reddit: Unlocking Social Media's Potential in Dietary Assessment.
Nutrients
Feb 27, 2025
: The increasing use of social media for sharing health and diet experiences presents new opportunities for nutritional research and dietary assessment. Large language models (LLMs) and artificial intelligence (AI) offer innovative approaches to anal...
Weight loss-independent changes in human growth hormone during water-only fasting: a secondary evaluation of a randomized controlled trial.
Frontiers in endocrinology
Feb 7, 2025
INTRODUCTION: Water-only fasting for one day or more may provide health benefits independent of weight loss. Human growth hormone (HGH) may play a key role in multiple fasting-triggered mechanisms. Whether HGH changes during fasting are independent o...
The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.
Cardiovascular therapeutics
Oct 4, 2024
The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol contents, against oxidative stress and obesity in a rat model following a high-fat-sugar diet (HFSD). The MPT was prepared through the customary method...
Prediction of individual weight loss using supervised learning: findings from the CALERIE 2 study.
The American journal of clinical nutrition
Sep 11, 2024
BACKGROUND: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual.
The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.
Clinical nutrition (Edinburgh, Scotland)
Apr 5, 2024
BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weig...
Outcomes of totally robotic Roux-en-Y gastric bypass in patients with BMI ≥ 50 kg/m: can the robot level out "traditional" risk factors?
Journal of robotic surgery
Oct 10, 2023
Roux-en-Y gastric bypass (RYGB) in patients with body mass index (BMI) ≥ 50 kg/m is a challenging procedure and BMI ≥ 50 kg/m has been identified as independent risk factor for postoperative complications and increased morbidity in previous studies. ...
An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.
Journal of medical Internet research
Sep 6, 2023
BACKGROUND: Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatm...
Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries.
Journal of magnetic resonance imaging : JMRI
Feb 20, 2023
BACKGROUND: Diagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision-making.
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