AIMC Topic: Asthma
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Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.
Nature communications
Mar 26, 2025
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lip...
Factors associated with the development of severe asthma: A nationwide study (FINASTHMA).
Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology
Mar 9, 2025
BACKGROUND: Severe asthma presents a major challenge to health care and negatively affects the quality of life of patients. Understanding the factors predicting the development of severe asthma is limited.
Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.
Journal of medical systems
Mar 4, 2025
Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecti...
Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning-Based Sentiment Analysis.
Journal of medical Internet research
Feb 13, 2025
BACKGROUND: Online health communities (OHCs) enable people with long-term conditions (LTCs) to exchange peer self-management experiential information, advice, and support. Engagement of "superusers," that is, highly active users, plays a key role in ...
Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.
Scientific reports
Feb 6, 2025
Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical da...
Machine learning models for preventative mobile health asthma control.
The Journal of asthma : official journal of the Association for the Care of Asthma
Jan 24, 2025
INTRODUCTION: Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technolog...
Assessing ChatGPT's accuracy and reliability in asthma general knowledge: implications for artificial intelligence use in public health education.
The Journal of asthma : official journal of the Association for the Care of Asthma
Jan 11, 2025
BACKGROUND: Integrating Artificial Intelligence (AI) into public health education represents a pivotal advancement in medical knowledge dissemination, particularly for chronic diseases such as asthma. This study assesses the accuracy and comprehensiv...
Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.
European respiratory review : an official journal of the European Respiratory Society
Jan 8, 2025
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...
Predicting Asthma Exacerbations Using Machine Learning Models.
Advances in therapy
Nov 18, 2024
INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and ...
Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.
European respiratory review : an official journal of the European Respiratory Society
Nov 13, 2024
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
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