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This paper presents a hybrid framework that combines structured clinical data with LLM-generated narratives for coronary artery disease prediction, achieving high fidelity in variable extraction and comparing ML models with LLM-based zero-shot and few-shot classification.
This paper presents a machine learning framework using CatBoost and SHAP to predict obstructive coronary artery disease from CT calcium scoring scans, achieving high accuracy by combining calcium-omics and epicardial fat features.