XAI for Better Exploitation of Text in Medical Decision Support
Ajay Madhavan Ravichandran, Julianna Grune, Nils Feldhus, Aljoscha Burchardt, Roland Roller, Sebastian Möller
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.- Anthology ID:
- 2024.bionlp-1.41
- Volume:
- Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
- Venues:
- BioNLP | WS
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 506–513
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.41
- DOI:
- Cite (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.
- Cite (Informal):
- XAI for Better Exploitation of Text in Medical Decision Support (Ravichandran et al., BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.bionlp-1.41.pdf