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Optimizing the Diagnostic Algorithm for Pulmonary Embolism in Acute COPD Exacerbation Using Fuzzy Rough Sets and

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence in Medicine
  • Respiratory Medicine

Background:

  • Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) presents diagnostic challenges, particularly in identifying comorbid pulmonary embolism (PE).
  • Accurate and timely PE diagnosis in AECOPD patients is crucial for effective treatment and improved outcomes.
  • Current diagnostic methods may lack sufficient accuracy or efficiency in this specific patient population.

Purpose of the Study:

  • To develop and validate an optimized diagnostic model for PE in AECOPD patients.
  • To improve the prediction accuracy and stability of PE diagnosis compared to existing methods.
  • To provide a tool that assists clinicians in making timely treatment decisions.

Main Methods:

  • A retrospective study involving 185 AECOPD patients, with 90 diagnosed with PE via CTPA.
  • Utilized Fuzzy Rough Sets (FRS) to quantify indicator importance for PE diagnosis.
  • Constructed a Support Vector Machine (SVM) model (FRS-SVM) using selected key indicators.
  • Compared the FRS-SVM model's performance against a logistic regression model.

Main Results:

  • The FRS-SVM model achieved an average accuracy of 94.67% and an AUC of 0.944 across 10 independent trials.
  • The logistic regression model achieved an average accuracy of 80.41% and an AUC of 0.809.
  • The FRS-SVM model demonstrated significantly higher accuracy and stability in diagnosing PE in AECOPD patients.

Conclusions:

  • The developed FRS-SVM model offers superior accuracy and reliability for PE diagnosis in AECOPD patients.
  • This model can enhance pre-CTPA prediction probability, aiding clinical decision-making.
  • The FRS-SVM model shows potential for integration into clinical practice to optimize PE diagnosis in AECOPD.