@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Is Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients
%A El Khettari, Oumaima
%A Barthet, Virgile
%A Hocquet, Guillaume
%A Weller, Joconde
%A Morin, Emmanuel
%A Zweigenbaum, Pierre
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F el-khettari-etal-2026-clinical
%X 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.
%R 10.63317/47hsfchk79n6
%U https://aclanthology.org/2026.lrec-1.14/
%U https://doi.org/10.63317/47hsfchk79n6
%P 194-206
Markdown (Informal)
[Is Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients](https://aclanthology.org/2026.lrec-1.14/) (El Khettari et al., LREC 2026)
ACL