Chronic obstructive pulmonary disease (COPD) represents a significant global health burden, where precise severity assessment is particularly critical for effective clinical management in intensive care unit (ICU) settings. This study introduces an innovative machine learning framework for COPD severity classification utilizing the MIMIC-III critical care database, thereby expanding the applications of artificial intelligence in critical care medicine. Our research developed a robust classification model incorporating key ICU parameters such as blood gas measurements and vital signs, while implementing semi-supervised learning techniques to effectively utilize unlabeled data and enhance model performance. The random forest classifier emerged as particularly effective, demonstrating exceptional discriminative capability with 92.51% accuracy and 0.98 ROC AUC in differentiating between mild-to-moderate and severe COPD cases. This machine learning approach provides clinicians with a practical, accurate, and efficient tool for rapid COPD severity evaluation in ICU environments, with significant potential to improve both clinical decision-making processes and patient outcomes. Future research directions should prioritize external validation across diverse patient populations and integration with clinical decision support systems to optimize COPD management in critical care settings.
View on arXiv@article{shojaei2025_2504.18593,
title={ Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset },
author={ Akram Shojaei and Mehdi Delrobaei },
journal={arXiv preprint arXiv:2504.18593},
year={ 2025 }
}