AIMC Topic: Asthma
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A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model.
PloS one
Aug 21, 2025
Asthma is a frequent and long-lasting disorder associated with airway inflammation. The disease severity may lead to serious health concerns and even mortality. In this work, we propose a novel hybrid approach using machine learning models and simila...
Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study.
Scientific reports
Aug 19, 2025
Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and m...
Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.
Scientific reports
Aug 6, 2025
Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limited by factors such as patient cooperation with spirometry. Non-invasive acoustic analysis using machine learning offers a promising alternative for ob...
Anoikis-related biomarkers PARP1 and SDCBP as diagnostic and therapeutic targets for asthma.
Scientific reports
Jul 9, 2025
This study aims to explore the association between anoikis-related genes (ARGs) and asthma. The dataset GSE143303 for asthma were sourced from the GEO database, while ARGs were retrieved from the Harmonizome web portal and the GeneCards database. Dif...
Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.
BMJ health & care informatics
Jul 7, 2025
OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.
Breath profiles in paediatric allergic asthma by proton transfer reaction mass spectrometry.
BMJ open respiratory research
Jun 27, 2025
INTRODUCTION: Enhancing paediatric asthma diagnosis is crucial. Molecular analysis of exhaled breath is a rapidly evolving field aimed at harnessing established and innovative technologies for clinical applications. This study evaluates the feasibili...
Altered static and dynamic functional network connectivity and combined Machine learning in asthma.
Neuroscience
Apr 26, 2025
Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structural and functional abnormalities in the brains of individuals with asthma. However, earlier research h...
Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.
NPJ primary care respiratory medicine
Apr 23, 2025
Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-ma...
Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.
BMC medical informatics and decision making
Apr 18, 2025
BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great poten...
Application and research progress of artificial intelligence in allergic diseases.
International journal of medical sciences
Apr 9, 2025
Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, De...
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