AIMC Topic: Asthma
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Machine learning implicates the IL-18 signaling axis in severe asthma.
JCI insight
Nov 8, 2021
Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has focused on identifying subgroups of patients with similar pathobiological underpinnings. Previous st...
Deep Learning Algorithms-Based CT Images in Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction.
Journal of healthcare engineering
Oct 21, 2021
CT image information data under deep learning algorithms was adopted to evaluate small airway function and analyze the clinical efficacy of different glucocorticoid administration ways in asthmatic children with small airway obstruction. The Res-NET ...
DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. Firs...
Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.
PloS one
Aug 2, 2021
RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials.
Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.
Methods of information in medicine
Jul 14, 2021
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 fr...
Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index.
Environmental pollution (Barking, Essex : 1987)
Jun 14, 2021
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 o...
Does machine learning have a role in the prediction of asthma in children?
Paediatric respiratory reviews
Jun 9, 2021
Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortun...
Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning.
IEEE journal of biomedical and health informatics
Jun 3, 2021
Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from comorbidity, the cost of treating such a patient becomes dependent on the nature of the comorbidity. T...
Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.
International journal of medical sciences
Jun 1, 2021
Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity...
Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology.
Frontiers in immunology
Apr 15, 2021
Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study ...
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