AIMC Topic: Asthma

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

The journal of allergy and clinical immunology. In practice Apr 27, 2024
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.
Machine Learning Phenotype Electronic Health Records Humans Adult Asthma Male Female Aged Young Adult Middle Aged Cluster Analysis Spirometry
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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 Apr 23, 2024
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...
Cough Tanzania Female Cross-Sectional Studies Humans Pulmonary Disease, Chronic Obstructive Male Artificial Intelligence Asthma Rural Population Machine Learning
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Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

International immunopharmacology Apr 11, 2024
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.
Machine Learning Biomarkers Asthma Humans Mice Computational Biology Databases, Genetic Animals Gene Expression Profiling Gene Regulatory Networks
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Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases.

Expert review of respiratory medicine Jan 25, 2024
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...
Artificial Intelligence Machine Learning Pulmonary Disease, Chronic Obstructive Humans Asthma
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Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

Pediatric research Jan 11, 2024
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.
Respiratory Sounds Birth Cohort Male Infant Child, Preschool Canada Longitudinal Studies Respiratory Tract Infections Infant, Newborn Risk Factors Asthma Humans Machine Learning Female
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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 Jan 8, 2024
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.
Child Dietary Fiber Eating Female Nutrition Surveys Potassium Rhinitis, Allergic Humans Asthma Iron Thiamine Republic of Korea Child, Preschool
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Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.

Advances in therapy Dec 19, 2023
The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advanc...
Patient Acuity Machine Learning Precision Medicine Asthma Humans Artificial Intelligence
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Deep learning prediction of hospital readmissions for asthma and COPD.

Respiratory research Dec 13, 2023
QUESTION: Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and evaluat...
Asthma Male Deep Learning Patient Readmission Female Hospitalization Pulmonary Disease, Chronic Obstructive Humans Retrospective Studies
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Pattern recognition of forced oscillation technique measurement results using deep learning can identify asthmatic patients more accurately than setting reference ranges.

Scientific reports Dec 7, 2023
No official clinical reference values have been established for MostGraph, which measures total respiratory resistance and reactance using the forced oscillation technique, complicating result interpretation. This study aimed to establish a reference...
Humans Asthma Spirometry Airway Resistance Deep Learning Reference Values Respiratory Function Tests
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Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

Respiratory research Dec 1, 2023
BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and i...
Algorithms Asthma Deep Learning Air Pollutants Air Pollution Influenza, Human Humans
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