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Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
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In terms of human respiration, the act of expelling air, known as exhalation (or expiration), operates on the principle of pressure gradients. During expiration, the pressure within the lungs exceeds that of the surrounding atmosphere. Under normal conditions, quiet breathing involves passive exhalation and is free of muscular contractions. This is because the exhalation process is driven by the natural elastic recoil of the lungs and chest wall, both of which have an inherent tendency to...
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Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Heidi Makrinioti1,2, Paraskevi Maggina3, John Lakoumentas3

  • 1West Middlesex University Hospital, Chelsea and Westminster Foundation Trust, Isleworth, United Kingdom.

Frontiers in Allergy
|April 7, 2022
PubMed
Summary

Machine learning models can predict wheeze exacerbations in infants after hospitalization for acute bronchiolitis. Rhinovirus and clinical severity are key predictors of persistent wheezing in these children.

Keywords:
bronchiolitismachine learningrhinovirusviruswheeze

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Area of Science:

  • Pediatrics
  • Infectious Diseases
  • Machine Learning in Healthcare

Background:

  • Acute bronchiolitis is a common infant respiratory infection.
  • Hospitalized infants with bronchiolitis have an increased risk of developing wheeze exacerbations.
  • Predicting persistent wheezing is crucial for early intervention and management.

Purpose of the Study:

  • To develop machine learning models for predicting the incidence and persistence of wheeze exacerbations.
  • To identify key predictors of wheeze exacerbations following a first hospitalized episode of acute bronchiolitis.

Main Methods:

  • Prospective cohort study of 131 hospitalized infants with bronchiolitis and 73 controls.
  • 3-year follow-up with 6-monthly telephone reviews.
  • Principal Component Analysis (PCA) and Random Forest classification were used to develop prediction models.

Main Results:

  • PCA identified two clusters of outcomes, with Cluster 1 associated with more wheeze exacerbations.
  • Rhinovirus (RV) detection was more common in Cluster 1 and linked to higher clinical severity.
  • A prediction model using virus type and clinical severity achieved 75.56% sensitivity and 91.86% specificity for Cluster 1.

Conclusions:

  • A prediction model incorporating virus type and clinical severity can effectively predict wheeze exacerbations after hospitalization for bronchiolitis.
  • Rhinovirus emerged as the strongest predictor of wheeze exacerbations.