RadCoref: Fine-tuning coreference resolution for different styles of clinical narratives (version 1.0.0)

Liao, Yuxiang, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2024. RadCoref: Fine-tuning coreference resolution for different styles of clinical narratives (version 1.0.0). [Online]. PhysioNet. Available at: https://doi.org/10.13026/z67q-xy65

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Abstract

RadCoref is a small subset of MIMIC-CXR with manually annotated coreference mentions and clusters. The dataset is annotated by a panel of three cross-disciplinary experts with experience in clinical data processing following the i2b2 annotation scheme with minimum modification. The dataset consists of Findings and Impression sections extracted from full radiology reports. The dataset has 950, 25 and 200 section documents for training, validation, and testing, respectively. The training and validation sets are annotated by one annotator. The test set is annotated by two human annotators independently, of which the results are merged manually by the third annotator. The dataset aims to support the task of coreference resolution on radiology reports. Given that the MIMIC-CXR has been de-identified already, no protected health information (PHI) is included.

Item Type:	Website Content
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	PhysioNet
Date of Acceptance:	2024
Last Modified:	08 Feb 2024 15:58
URI:	https://orca.cardiff.ac.uk/id/eprint/165980

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