RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports

Jean-Benoit Delbrouck, Pierre Chambon, Zhihong Chen, Maya Varma, Andrew Johnston, Louis Blankemeier, Dave Van Veen, Tan Bui, Steven Truong, Curtis Langlotz


Abstract
In order to enable extraction of structured clinical data from unstructured radiology reports, we introduce RadGraph-XL, a large-scale, expert-annotated dataset for clinical entity and relation extraction. RadGraph-XL consists of 2,300 radiology reports, which are annotated with over 410,000 entities and relations by board-certified radiologists. Whereas previous approaches focus solely on chest X-rays, RadGraph-XL includes data from four anatomy-modality pairs - chest CT, abdomen/pelvis CT, brain MR, and chest X-rays. Then, in order to automate structured information extraction, we use RadGraph-XL to train transformer-based models for clinical entity and relation extraction. Our evaluations include comprehensive ablation studies as well as an expert reader study that evaluates trained models on out-of-domain data. Results demonstrate that our model surpasses the performance of previous methods by up to 52% and notably outperforms GPT-4 in this domain. We release RadGraph-XL as well as our trained model to foster further innovation and research in structured clinical information extraction.
Anthology ID:
2024.findings-acl.765
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12902–12915
Language:
URL:
https://aclanthology.org/2024.findings-acl.765
DOI:
10.18653/v1/2024.findings-acl.765
Bibkey:
Cite (ACL):
Jean-Benoit Delbrouck, Pierre Chambon, Zhihong Chen, Maya Varma, Andrew Johnston, Louis Blankemeier, Dave Van Veen, Tan Bui, Steven Truong, and Curtis Langlotz. 2024. RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports. In Findings of the Association for Computational Linguistics ACL 2024, pages 12902–12915, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports (Delbrouck et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2024.findings-acl.765.pdf