Asthma

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.

OBJECTIVES: Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., Internationa...

Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index.

Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood asthma. The aim of this study was to use a novel greenness exposure assessment method, capable of overcoming the limitation of NDVI to determine the extent to which it was associated with asthma prevalence in Chinese children. During 2009-2013, a cr...

Does machine learning have a role in the prediction of asthma in children?

Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify childr...

Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning.

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from co...

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing count...

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study.

OBJECTIVE: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization a...

Exploration of machine learning techniques to examine the journey to neuroendocrine tumor diagnosis with real-world data.

Machine learning reveals pathways to neuroendocrine tumor (NET) diagnosis. Patients with NET and age-/gender-matched non-NET controls were retrospec...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allergen. (Bt) is a prevalent allergen in tropical regio...

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.

BACKGROUND: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of ...

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology.

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise dia...

Identification of pediatric respiratory diseases using a fine-grained diagnosis system.

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clin...

Machine learning in asthma research: moving toward a more integrated approach.

: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analyt...

Personalized prediction of early childhood asthma persistence: A machine learning approach.

Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to id...

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains ...

Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks.

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The econ...

Asthma-prone areas modeling using a machine learning model.

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased....

Novel Machine Learning Can Predict Acute Asthma Exacerbation.

BACKGROUND: Asthma exacerbations result in significant health and economic burden, but are difficult to predict.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data.

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recent...

Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores.

PURPOSE: To develop radiomics-based CT scores for assessing lung disease severity and exacerbation risk in adult patients with cystic fibrosis (CF).

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to ...