Primary Care

Obesity

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

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Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.

OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machi...

Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach.

Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered...

Predicting Prostate Cancer Diagnosis Using Machine Learning Analysis of Healthcare Utilization Patterns.

This study investigated healthcare utilization patterns prior to prostate cancer diagnoses, aiming t...

Artificial Intelligence-Based Diets: A Role in the Nutritional Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease?

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global hea...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Incident Hypertension.

IMPORTANCE: Hypertension underpins significant global morbidity and mortality. Early lifestyle inter...

Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning.

Elevated consumption of sugar-sweetened beverages (SSBs) has been associated with an increase in obe...

[Artificial intelligence in assessment of individual risks of age-related macular degeneration progression].

Age-related macular degeneration (AMD) is a progressive degenerative retinal disease and a leading c...

Predicting metabolic syndrome: Machine learning techniques for improved preventive medicine.

Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group...

Estimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method.

The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are c...

Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms.

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on...

Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.

Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. H...

Leveraging artificial intelligence for advancements in reproductive health.

We are writing to address the growing interest in the role of artificial intelligence (AI) within he...

Predicting phage-host interactions via feature augmentation and regional graph convolution.

Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is t...

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment.

Currently, eight million people in the United States suffer from cancer and it is a major global hea...

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.

BACKGROUND: The demand for fresh strategies to analyze intricate multidimensional data in neuroscien...

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