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• Enriching clinical text with entity-level representations improves mortality prediction over using CLS embeddings alone in heart failure patients.result 90%
• Supervised multimodal fusion of text and structured variables achieves the best overall performance for short-term mortality prediction in heart failure.result 90%
• Large language models perform inconsistently across modalities and decoding strategies for heart failure mortality prediction.limitation 85%
• Text-only prompts outperform structured or multimodal inputs when using large language models for heart failure mortality prediction.result 85%
• Entity-aware multimodal transformers offer the most reliable solution for short-term heart failure outcome prediction.technical 90%
• Current LLM prompting remains limited for clinical decision support in heart failure mortality prediction.limitation 85%
• Accurate short-term mortality prediction in heart failure remains challenging when relying on structured electronic health record data alone.limitation 80%

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