AIMC Topic: Asthma
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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.
Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.
Autoimmunity
Nov 12, 2024
Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by d...
Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses.
The Journal of asthma : official journal of the Association for the Care of Asthma
Oct 12, 2024
BACKGROUND: Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlyin...
Tracing the path from preschool wheezing to asthma.
Pediatric pulmonology
Oct 3, 2024
This short review illustrates, using two recent studies, the potential and challenges of using machine learning methods to identify phenotypes of wheezing and asthma from childhood onwards.
Artificial Intelligence-Supported Development of Health Guideline Questions.
Annals of internal medicine
Sep 24, 2024
BACKGROUND: Guideline questions are typically proposed by experts.
Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.
Respiratory research
Aug 31, 2024
BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecula...
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