Obesity

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs.

Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (...

Prediction of Fatty Acid Intake from Serum Fatty Acid Levels Using Machine Learning Technique in Women Living in Toyama Prefecture.

Preventing lifestyle-related diseases requires understanding and managing the intake of total fats and specific types of fatty acids, especially trans fatty acids. There are several methods for measuring fat intake, each with its own strengths and limitations. Guidelines for nutritional epidemiology studies recommend employing objective biomarkers. This study aimed to estimate fatty acid intake ba...

Development A Novel Classification Based on Serum Sodium Level Integrated with Comorbid Conditions (BASIC) in Hyponatremia Patients Via Data-Driven Cluster Analysis.

Hyponatremia was defined as a serum sodium level below 136 mmol/L. The current classification identified serum sodium level below 120 mmol/L as high-r...

Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.

To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learn...

Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.

In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified defini...

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The ma...

Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study.

PURPOSE: Metabolic bariatric surgery (MBS) became integral to managing severe obesity. Understanding surgical risks associated with MBS is crucial. Di...

Prediction of individual weight loss using supervised learning: findings from the CALERIE 2 study.

BACKGROUND: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians sel...

Comorbidity-based framework for Alzheimer's disease classification using graph neural networks.

Alzheimer's disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Current deep learning approaches, pa...

Digital therapeutics in hypertension: How to make sustainable lifestyle changes.

Various digital therapeutic products have been validated and approved since 2017. They have demonstrated efficacy and safety as a new therapeutic moda...

Association of N-terminal pro-B natriuretic peptide with all-cause mortality and cardiovascular mortality in obese and non-obese populations and the development of a machine learning prediction model: National Health and Nutrition Examination Survey (NHANES) 1999-2004.

AIMS: To explore the potential of N-terminal pro-B natriuretic peptide (NTproBNP) in identifying adverse outcomes, particularly cardiovascular adverse...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedent...

Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.

Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fastin...

Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements.

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based obesity identification framework that integrates sm...

Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model.

INTRODUCTION: Pharmacologic therapies for symptoms of gastroparesis (GP) have limited efficacy, and it is difficult to predict which patients will res...

Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence.

Various extrinsic and intrinsic factors such as drug exposures, antibiotic treatments, smoking, lifestyle, genetics, immune responses, and the gut mic...

The prediction of semen quality based on lifestyle behaviours by the machine learning based models.

PURPOSE: To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire da...

Obesity prediction: Novel machine learning insights into waist circumference accuracy.

AIMS: This study aims to enhance the precision of obesity risk assessments by improving the accuracy of waist circumference predictions using machine ...

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of...

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due ...