Primary Care

Obesity

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

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

Various extrinsic and intrinsic factors such as drug exposures, antibiotic treatments, smoking, life...

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 se...

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...

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 f...

Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence.

Established life course approaches suggest that health status in adulthood can be influenced by even...

Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression.

The controversy surrounding whether serum total cholesterol is a risk factor for the graded progress...

Epidemiological breast cancer prediction by country: A novel machine learning approach.

Breast cancer remains a significant contributor to cancer-related deaths among women globally. We se...

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predicto...

Detection of Lungs Tumors in CT Scan Images Using Convolutional Neural Networks.

Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is can...

Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts.

BACKGROUND: The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptu...

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalitie...

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics.

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinan...

Navigating the future of health care with AI-driven digital therapeutics.

Digital therapeutics (DTx) is a recently conceived idea in health care that aims to cure ailments an...

A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfu...

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a...

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