AIMC Topic: Asthma
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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...
Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.
BMJ open
Aug 30, 2024
INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with p...
Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.
Journal of medical Internet research
Aug 23, 2024
BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even imp...
Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction.
Magnetic resonance in medicine
Aug 18, 2024
PURPOSE: To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized Xe lung ventilation MRI.
Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.
Pediatric pulmonology
Jul 29, 2024
RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes.
Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.
Pediatric pulmonology
Jul 23, 2024
OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway change...
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