Prediction of Heart Failure based on Multimodal Data from MIMIC-IV

Heart failure (HF) affects over 64 million people worldwide and remains a leading cause of cardiovascular mortality. Early identification of patients at risk is essential for timely treatment and to support hospital and primary care physicians. This study compares XGBoost and a Transformerbased bidirectional cross-attention model using multimodal data to assess whether deep learning (DL) approaches can outperform classical machine learning (ML) methods for early HF prediction. We identified HF and non-HF patients from the MIMIC-IV database using ICD-9/10 codes, supplemented by clinical evidence from laboratory results, radiology, and discharge notes. Furthermore, we defined a 48-hour prediction window prior to the first clinical evidence of HF. Structured features were engineered from rolling-window statistics and clinical thresholds. Both XGBoost and Transformer models were trained on multimodal data and compared through an ablation study. Finally, we developed a dashboard using a small set of laboratory and medication features to deliver a 48- hour HF risk estimate, aiding clinician diagnosis. Multimodal models outperformed single-modality models across both architectures. The multimodal XGBoost model achieved the highest performance (F1 of 0. 8773 and PR-AUC of 0.9402), while the multimodal Transformer achieved slightly lower performance (F1 0.8635, PR-AUC 0.9209). Structured data contributed most to XGBoost (PR-AUC of 0.9163), whereas clinical notes were better captured by the Transformer (PRAUC of 0.8220). Explainable dashboards further enhance transparency and usability by delivering quantitative 48-hour risk estimates from minimal features. This demonstrates, in this setting, that traditional ML can outperform DL models such as Transformers on tabular-dominated, multimodal clinical prediction tasks while preserving interpretability, underscoring decision-support systems’ potential to aid timely diagnosis.