Primary Care

Obesity

Latest AI and machine learning research in obesity for healthcare professionals.

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Showing 358-378 of 1,198 articles
Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome ...

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 opt...

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...

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

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predic...

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.

OBJECTIVE AND AIMS: Identification of associations between the obese category of weight in the gener...

Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis.

AIM: Machine learning may be a tool with the potential for obesity prediction. This study aims to re...

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

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Weste...

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients wi...

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial i...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure ...

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...

Stacked neural network for predicting polygenic risk score.

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility fr...

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 preve...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim o...

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

BACKGROUND: Prediabetes is a highly prevalent condition that heralds an increased risk of progressio...

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 bo...

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 o...

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.

BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening a...

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