AIMC Topic: Asthma

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Use of a Robotic Sampler (PIPER) for Evaluation of Particulate Matter Exposure and Eczema in Preschoolers.

International journal of environmental research and public health
While the association of eczema with asthma is well recognized, little research has focused on the potential role of inhalable exposures and eczema. While indoor air quality is important in the development of respiratory disease as children in the U....

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

PloS one
Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly bein...

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification.

PloS one
BACKGROUND: Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating the...

Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease.

BMC medical informatics and decision making
BACKGROUND: This paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network.

Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative Causal Forest to Identify Subgroups.

Pharmacoepidemiology and drug safety
BACKGROUND: Glucagon-like Peptide-1 Receptor Agonists (GLP1RA) may reduce asthma exacerbation (AE) risk, but it is unclear which populations benefit most. Recent pharmacoepidemiologic studies have employed iterative causal forest (iCF), a machine lea...

Single-cell RNA sequencing reveals immunological link between house dust mite allergy and childhood asthma.

Scientific reports
Allergic asthma in children is typically associated with house dust mites (HDM) as the key allergen. Nevertheless, the diagnostic rate remains below 60% due to the absence of specific symptoms and diagnostic markers, which hinders the implementation ...

Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.

European respiratory review : an official journal of the European Respiratory Society
Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and monitoring. In respiratory care, evidence supporting their clinical use remains fragmented and inco...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

Bioinformatics (Oxford, England)
SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling te...

Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions.

Studies in health technology and informatics
Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented with machine learning, it is becoming possible to replace traditional tedious active monitoring in c...

Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis.

Medicine
This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) outpatient treatment support in terms of patient health and public costs. The data obtained in the res...