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

Year: 2025

Type: article

Abstract: 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.

Source: iScience

DOI: 10.1016/j.isci.2025.114197