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Related Concept Videos

Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

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.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
Asthma-IV: Diagnostic and Management01:30

Asthma-IV: Diagnostic and Management

The diagnosis and management of asthma are comprehensive, encompassing clinical assessments, lung function tests, and pharmacological interventions. Here's an overview:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
Asthma-I: Introduction01:29

Asthma-I: Introduction

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...
Asthma III: Clinical Manifestations01:13

Asthma III: Clinical Manifestations

Asthma presents with a characteristic pattern of episodic respiratory symptoms that reflect underlying airway inflammation, bronchoconstriction, and mucus hypersecretion. Although severity varies among individuals, certain clinical manifestations are considered hallmarks of the disorder and often guide diagnosis and assessment.Respiratory SymptomsA persistent cough is one of the most common early features of asthma. It is frequently dry and tends to worsen at night or in the early morning,...
Asthma I: Introduction01:28

Asthma I: Introduction

Asthma is a chronic inflammatory disorder of the airways characterized by variable airflow obstruction and heightened bronchial responsiveness to a wide range of triggers. The underlying inflammation leads to airway swelling, mucus hypersecretion, and smooth muscle constriction, all of which narrow the airway lumen and impede airflow. Clinically, asthma presents with recurrent episodes of wheezing, shortness of breath, chest tightness, and coughing, symptoms that typically vary in intensity and...
Asthma: Pathogenesis and Management01:20

Asthma: Pathogenesis and Management

Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
Asthma is classified as allergic and non-allergic. Allergens such as dust mites, pollen, and pet dander trigger allergic asthma, while factors like cold air, intense emotions, or exercise can induce non-allergic asthma.

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Related Experiment Video

Updated: Jul 12, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Internally validated machine learning models identify asthma and its phenotypes using multicenter real-world data.

Rongfang Tu1,2, Sha Liu2, Xiaowu Tan2

  • 1Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.

Scientific Reports
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models effectively identify asthma and its subtypes using routine clinical data. LASSO regression showed superior performance for phenotyping, aiding personalized asthma treatment strategies.

Keywords:
AsthmaLASSO regressionMachine learningPhenotypeRandom forestReal-world data

Related Experiment Videos

Last Updated: Jul 12, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Computational biology
  • Medical informatics
  • Pulmonology

Background:

  • Asthma presents diverse clinical features, requiring precise phenotyping for tailored treatments.
  • Routine clinical data offers a rich, underutilized resource for asthma identification and subtyping.

Purpose of the Study:

  • To develop and validate machine learning models for asthma identification and phenotyping using electronic health records.
  • To compare the performance of LASSO regression and random forest models in this task.

Main Methods:

  • Integrated data from MIMIC-IV and OPCRD databases, including 3,025 cases.
  • Extracted 30 baseline variables (demographics, symptoms, biomarkers, genetics).
  • Developed LASSO regression and random forest models, evaluating performance with AUC and feature importance.

Main Results:

  • LASSO models outperformed random forest, achieving AUCs of 0.836 for overall asthma, 0.782 for type 2, 0.751 for allergic, and 0.723 for early-onset asthma.
  • LASSO demonstrated statistically significant advantages for type 2 and early-onset asthma identification.
  • Key predictors included allergy history, family history of asthma, wheezing frequency, bronchodilator reversibility, age, FeNO, IgE, and pulmonary function indices.

Conclusions:

  • Routine clinical data can effectively identify asthma and its subtypes using machine learning.
  • LASSO regression offers a robust and interpretable approach for asthma phenotyping, supporting potential EMR integration.
  • Further external validation is necessary to confirm generalizability.