AI Medical Compendium Topic

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Asthma

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Developments in respiratory self-management interventions over the last two decades.

Chronic respiratory disease
This paper describes developments in the fields of asthma and COPD self-management interventions (SMIs) over the last two decades and discusses future directions. Evidence around SMIs has exponentially grown. Efficacy on group level is convincing and...

Sputum Neutrophil Gelatinase-Associated Lipocalin as a Biomarker in Asthma-COPD Overlap.

The Journal of the Association of Physicians of India
BACKGROUND: Asthma COPD overlap (ACO) is a consensus-based phenotype having characteristics of both COPD and asthma. Distinguishing ACO from other diseases is even more important as it is related to low health-related quality of life, augmented exace...

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

The Journal of allergy and clinical immunology
BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways.

Association between household income levels and nutritional intake of allergic children under 6 years of age in Korea: 2019 Korea National Health and Nutrition Examination Survey and application of machine learning.

Frontiers in public health
INTRODUCTION: This study investigated the prevalence of allergic diseases in Korean children aged 6 and below, focusing on the interplay between nutritional status, household income levels, and allergic disease occurrence.

Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases.

Expert review of respiratory medicine
INTRODUCTION: Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burde...

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

Pediatric research
BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma.

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

The journal of allergy and clinical immunology. In practice
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

JMIR research protocols
BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and m...

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

International immunopharmacology
BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

Journal of medical systems
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in...