AIMC Topic: Anti-Asthmatic Agents

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An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning.

Journal of medical systems Apr 26, 2019
Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best metho...
Anti-Asthmatic Agents Bronchodilator Agents Iran Respiratory Function Tests Environment Asthma Severity of Illness Index Socioeconomic Factors Supervised Machine Learning Genetic Predisposition to Disease Algorithms Biomarkers Humans
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Efficacy and safety of lebrikizumab in adult patients with mild-to-moderate asthma not receiving inhaled corticosteroids.

Respiratory medicine Dec 13, 2017
BACKGROUND: Asthma is a heterogeneous and complex disease in both its clinical course and response to treatment. IL-13 is central to Type 2 inflammation and contributes to many features of asthma. In a previous Phase 2 study, lebrikizumab, an anti-IL...
Anti-Asthmatic Agents Cyclopropanes Male Quinolines Antibodies, Monoclonal Middle Aged Injections, Subcutaneous Administration, Inhalation Acetates Glucocorticoids Treatment Outcome Humans Severity of Illness Index Sulfides Adult Female Double-Blind Method Treatment Failure Asthma Forced Expiratory Volume
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High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status.

Cell reports Apr 13, 2021
Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometry and machine learning to bronchoalveolar lavage (BAL) cells, we find that corticosteroid-resistant ...
Machine Learning Interleukin-10 Macrophages Humans Bronchoalveolar Lavage Fluid Adrenal Cortex Hormones Gene Expression Regulation Interferon-gamma Immunologic Memory Interleukin-7 Receptors, IgE Anti-Asthmatic Agents Adaptive Immunity Proteomics Gene Expression Profiling Asthma Signal Transduction Case-Control Studies Severity of Illness Index CD8-Positive T-Lymphocytes Immunity, Innate CD4-Positive T-Lymphocytes
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Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

Annals of the American Thoracic Society Jan 1, 2018
RATIONALE: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based o...
Bronchodilator Agents Machine Learning Algorithms Precision Medicine Female Phenotype Humans Treatment Outcome Severity of Illness Index Pediatric Obesity Child Models, Biological Asthma Anti-Asthmatic Agents Eosinophils Male
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