AI Medical Compendium Topic

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Machine learning insights on activities of daily living disorders in Chinese older adults.

Experimental gerontology
OBJECTIVE: This study on the aged population in China first used a large-scale longitudinal survey database to explore how different life factors affect their ability to engage in daily activities. We select and integrate multiple machine models to o...

Risk management of patients with multiple CVDs: what are the best practices?

Expert review of cardiovascular therapy
INTRODUCTION: Managing patients with multiple risk factors for CVDs can present distinct challenges for healthcare providers, therefore addressing them can be paramount to optimize patient care.

Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.

BMC public health
BACKGROUND: Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

Journal of psychosomatic research
PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to eluc...

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.

Computers in biology and medicine
BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic r...

The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.

BMC public health
BACKGROUND: Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions....

Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

BMC public health
BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning ...

Relationship between lifestyle factors and cardiovascular disease prevalence in Somaliland: A supervised machine learning approach using data from Hargeisa Group Hospital, 2024.

Current problems in cardiology
BACKGROUND: Cardiovascular diseases (CVDs) are leading contributors to global morbidity and mortality, with low- and middle-income countries experiencing disproportionately high burdens. In Somaliland, urbanization and lifestyle transitions have incr...

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.

Scientific reports
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. a...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

JMIR medical informatics
BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be e...