Asthma

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease sev...

Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.

BACKGROUND: The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential.

Deep learning prediction of hospital readmissions for asthma and COPD.

QUESTION: Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to ...

Pattern recognition of forced oscillation technique measurement results using deep learning can identify asthmatic patients more accurately than setting reference ranges.

No official clinical reference values have been established for MostGraph, which measures total respiratory resistance and reactance using the forced ...

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review.

BACKGROUND: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic hea...

Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.

BACKGROUND: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors us...

Phosphorylated p38 MAP kinase expression by leucocytes is increased in allergic humans and associated with IgE responses.

Mitogen-activated protein kinases (MAPK) activate cascades that regulate cell proliferation, differentiation and death. Phosphorylated (phos-)p38 MAPK...

Analyses of Factors Associated with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Review.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is the exacerbation of a range of respiratory symptoms during the stable phase o...

Hot topics in allergen immunotherapy, 2023: Current status and future perspective.

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in ...

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wh...

How AI is advancing asthma management? Insights into economic and clinical aspects.

Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particular...

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enric...

The water-soluble subfraction from Artemisia argyi alleviates LPS-induced inflammatory responses via multiple pathways and targets in vitro and in vivo.

ETHNOPHARMACOLOGICAL RELEVANCE: As a traditional Chinese medicine, Artemisia argyi has been used medicinally and eaten for more than 2000 years in Chi...

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature.

Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous ...

Zero time waste in pre-trained early exit neural networks.

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods s...

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques.

Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asth...

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.

Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe...

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.

The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow ...

Effects of secretome supplementation on interleukin-6, tumor necrosis factor-α, procalcitonin, and the length of stay in acute exacerbation COPD patients.

Acute exacerbation chronic obstructive pulmonary disease (AECOPD) is associated with significant poor survival. Mesenchymal stem cells (MSC) therapy h...

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of ...