AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure

Differentiating acute exacerbation of chronic obstructive pulmonary disease (AECOPD) from acute heart failure (AHF) is clinically challenging due to overlapping symptoms, especially in resource-limited settings lacking radiological/ultrasonographic tools. This study developed an eXtreme Gradient Boosting (XGBoost) model for differential diagnosis using Database: Medical Information Mart for Intensive Care (MIMIC) and two Chinese hospital cohorts, comparing it with a guideline-based model and applying Shapley Additive Explanations (SHAP) analysis to identify key biomarkers. The XGBoost model showed high discriminatory performance (area under the curve [AUC]: 0.94-0.98 across development/validation, outperforming the guideline-based model's AUC of 0.53) with consistent accuracy across age/sex subgroups. Key biomarkers included NT-proBNP and total bilirubin. This robust model enables rapid, accurate differential diagnosis in resource-constrained emergency settings.