Pulmonology

Asthma

Latest AI and machine learning research in asthma for healthcare professionals.

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Showing 64-84 of 1,587 articles
Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes...

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impac...

Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule ou...

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its s...

Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence-Assisted ARIA Care Pathways (ARIA 2024).

The traditional healthcare model is focused on diseases (medicine and natural science) and does not ...

Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular ly...

Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis.

INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chroni...

Preservative contact allergy in occupational dermatitis: a machine learning analysis.

Occupational dermatoses impose a significant socioeconomic burden. Allergic contact dermatitis relat...

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 millio...

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide,...

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized th...

Exploiting protein language models for the precise classification of ion channels and ion transporters.

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct sys...

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine...

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT.

PURPOSE: Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death ...

Visual Outcomes after Suprasellar Meningioma Resection: A Retrospective Cohort Study and a Machine Learning-Based Predictive Model.

 In this research, the authors provide a retrospective cohort study of 82 patients with suprasellar...

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obs...

A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhi...

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