@inproceedings{ravichandran-etal-2024-xai,
title = "{XAI} for Better Exploitation of Text in Medical Decision Support",
author = {Ravichandran, Ajay Madhavan and
Grune, Julianna and
Feldhus, Nils and
Burchardt, Aljoscha and
Roller, Roland and
M{\"o}ller, Sebastian},
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.41",
pages = "506--513",
abstract = "In electronic health records, text data is considered a valuable resource as it complements a medical history and may contain information that cannot be easily included in tables. But why does the inclusion of clinical texts as additional input into multimodal models, not always significantly improve the performance of medical decision-support systems? Explainable AI (XAI) might provide the answer. We examine which information in text and structured data influences the performance of models in the context of multimodal decision support for biomedical tasks. Using data from an intensive care unit and targeting a mortality prediction task, we compare information that has been considered relevant by XAI methods to the opinion of a physician.",
}
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%0 Conference Proceedings
%T XAI for Better Exploitation of Text in Medical Decision Support
%A Ravichandran, Ajay Madhavan
%A Grune, Julianna
%A Feldhus, Nils
%A Burchardt, Aljoscha
%A Roller, Roland
%A Möller, Sebastian
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ravichandran-etal-2024-xai
%X In electronic health records, text data is considered a valuable resource as it complements a medical history and may contain information that cannot be easily included in tables. But why does the inclusion of clinical texts as additional input into multimodal models, not always significantly improve the performance of medical decision-support systems? Explainable AI (XAI) might provide the answer. We examine which information in text and structured data influences the performance of models in the context of multimodal decision support for biomedical tasks. Using data from an intensive care unit and targeting a mortality prediction task, we compare information that has been considered relevant by XAI methods to the opinion of a physician.
%U https://aclanthology.org/2024.bionlp-1.41
%P 506-513
Markdown (Informal)
[XAI for Better Exploitation of Text in Medical Decision Support](https://aclanthology.org/2024.bionlp-1.41) (Ravichandran et al., BioNLP-WS 2024)
ACL
- Ajay Madhavan Ravichandran, Julianna Grune, Nils Feldhus, Aljoscha Burchardt, Roland Roller, and Sebastian Möller. 2024. XAI for Better Exploitation of Text in Medical Decision Support. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 506–513, Bangkok, Thailand. Association for Computational Linguistics.