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Chronic obstructive pulmonary disease (COPD) represents a significant globalhealth burden, where precise severity assessment is particularly critical foreffective clinical management in intensive care unit (ICU) settings. This studyintroduces an innovative machine learning framework for COPD severityclassification utilizing the MIMIC-III critical care database, therebyexpanding the applications of artificial intelligence in critical caremedicine. Our research developed a robust classification model incorporatingkey ICU parameters such as blood gas measurements and vital signs, whileimplementing semi-supervised learning techniques to effectively utilizeunlabeled data and enhance model performance. The random forest classifieremerged as particularly effective, demonstrating exceptional discriminativecapability with 92.51% accuracy and 0.98 ROC AUC in differentiating betweenmild-to-moderate and severe COPD cases. This machine learning approach providesclinicians with a practical, accurate, and efficient tool for rapid COPDseverity evaluation in ICU environments, with significant potential to improveboth clinical decision-making processes and patient outcomes. Future researchdirections should prioritize external validation across diverse patientpopulations and integration with clinical decision support systems to optimizeCOPD management in critical care settings.

Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset