Early auxiliary diagnosis model for chest pain triad based on artificial intelligence multimodal fusion.

Objectives: Acute chest pain is a common presentation in the emergency department, characterized by sudden onset with high morbidity and mortality. Traditional diagnostic methods, such as computed tomography (CT) and CT angiography (CTA), are often time-consuming and fail to meet the urgent need for rapid triage in emergency settings.

Materials and methods: We developed a multimodal model that integrates Bio-ClinicalBERT and ensemble learning (AdaBoost, Gradient boosting, and XGBoost) based on 41 382 patient data from April 1, 2013 to April 1, 2025 at Chongqing Daping Hospital. By integrating clinical texts and laboratory indicators, the model aims to classify the 3 major causes of fatal chest pain (acute coronary syndrome, pulmonary embolism, and aortic dissection), as well as other causes of chest pain, aiding rapid triage. We adopt strict data preprocessing and rank importance feature selection.

Results: The multimodal fusion model based on Gradient boosting exhibits the best performance: accuracy of 88.40%, area under the curve of 0.951, F1-score of 74.56%, precision of 77.50%, and recall of 72.52%. SHapley Additive exPlanations (SHAP) analysis confirmed the clinical relevance of key features such as d-dimer and high-sensitivity troponin. When reducing the number of numerical features to 30 key indicators, the model enhanced robustness without compromising performance.

Discussion and conclusion: We developed an artificial intelligence model for chest pain classification that effectively addresses the problem of overlapping clinical symptoms through multimodal fusion, and the model has high accuracy. However, future work needs to better integrate model development with clinical workflows and practical constraints.