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Related Concept Videos

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Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
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COPD is defined as a heterogeneous lung condition marked by persistent respiratory symptoms such as dyspnea, cough, and sputum production, caused by abnormalities in the airways that cause airflow obstruction.
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Related Experiment Video

Updated: Jul 17, 2025

Phenotyping Mouse Pulmonary Function In Vivo with the Lung Diffusing Capacity
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Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing

Fu-Qiang Ma1, Cong He2,3,4, Hao-Ran Yang5

  • 1Hubei University of Chinese Medicine, Wuhan, 430065, China.

BMC Medical Informatics and Decision Making
|August 29, 2023
PubMed
Summary

This study developed a machine learning model to predict pulmonary diffusing capacity impairment in COVID-19 survivors. The XGBoost model accurately identified key clinical factors like hemoglobin and maximal voluntary ventilation for predicting long-term lung function.

Keywords:
COVID-19Interpretable artificial intelligenceMachine learningMaximal voluntary ventilationPulmonary diffusing capacity impairment

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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
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Area of Science:

  • Pulmonary Medicine
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • COVID-19 survivors often experience pulmonary diffusing capacity impairment (PDCI).
  • Predicting PDCI is crucial for assessing long-term pulmonary function in COVID-19 survivors.
  • Current predictive models for PDCI in this population are limited.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting PDCI in COVID-19 convalescent patients.
  • To utilize routinely available clinical data for PDCI prediction.
  • To aid in the clinical diagnosis and management of post-COVID-19 pulmonary complications.

Main Methods:

  • A cohort of 221 COVID-19 survivors was studied 18 months post-discharge.
  • Data were randomly split into training (80%) and validation (20%) sets.
  • Six ML models were evaluated, with feature selection and data balancing techniques employed.

Main Results:

  • The XGBoost model demonstrated optimal performance with an AUC of 0.755 and 78.01% accuracy.
  • Hemoglobin (Hb), maximal voluntary ventilation (MVV), illness severity, platelet count (PLT), uric acid (UA), and blood urea nitrogen (BUN) were identified as key predictors.
  • SHAP analysis highlighted Hb and MVV as the most influential factors.

Conclusions:

  • The developed XGBoost model shows strong prognostic capability for PDCI in COVID-19 survivors.
  • Clinical factors, particularly Hb and MVV, are significant predictors of long-term pulmonary function after COVID-19.
  • This ML approach can assist clinicians in identifying survivors at risk for PDCI.