@inproceedings{el-khettari-etal-2026-clinical,
    title = "Is Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients",
    author = "El Khettari, Oumaima  and
      Barthet, Virgile  and
      Hocquet, Guillaume  and
      Weller, Joconde  and
      Morin, Emmanuel  and
      Zweigenbaum, Pierre",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lrec-1.14/",
    doi = "10.63317/47hsfchk79n6",
    pages = "194--206",
    abstract = "Accurate short-term mortality prediction in heart failure (HF) remains challenging, particularly when relying on structured electronic health record (EHR) data alone. We evaluate transformer-based models on a French HF cohort, comparing text-only, structured-only, multimodal, and LLM-based approaches. Our results show that enriching clinical text with entity-level representations improves prediction over CLS embeddings alone, and that supervised multimodal fusion of text and structured variables achieves the best overall performance. In contrast, large language models perform inconsistently across modalities and decoding strategies, with text-only prompts outperforming structured or multimodal inputs. These findings highlight that entity-aware multimodal transformers offer the most reliable solution for short-term HF outcome prediction, while current LLM prompting remains limited for clinical decision support."
}