Asthma

Managing chronic disease in the digital era: The role of telemedicine apps and platforms.

Chronic diseases such as diabetes, heart disease, or asthma are one of the biggest health challenges of the modern world. Their management requires constant care and monitoring, which is a serious burden for healthcare systems. In the digital era, the development of telemedicine, health applications, and telemedicine platforms creates new opportunities in the treatment and monitoring of patients w...

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

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 has primarily focused on static changes in brain activity, neglecting the effects of asthma on the dynamic characteristics of functional brain networks...

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool w...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of l...

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's heal...

Application and research progress of artificial intelligence in allergic diseases.

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textu...

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the ...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for indi...

A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.

Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine resp...

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for re...

Factors associated with the development of severe asthma: A nationwide study (FINASTHMA).

BACKGROUND: Severe asthma presents a major challenge to health care and negatively affects the quality of life of patients. Understanding the factors ...

Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from populatio...

Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program.

COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive ...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope ...

Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability.

Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical resp...

Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.

Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML)...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the forme...

Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction.

Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive ...

Machine learning models for preventative mobile health asthma control.

INTRODUCTION: Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. Th...

Analysis of the sensitization activity of leaves protein.

The determination of allergenic proteins in leaves, which is the main components of immune activity, has enabled the development of a more effective ...