Rani, Somiya; Jain, Amita; Kumar, Akshi and Yang, Guang. 2024. CCheXR-Attention: Clinical concept extraction and chest x-ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention. International Journal of Imaging Systems and Technology, 34(1), e23025. ISSN 0899-9457 [Article]

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Abstract or Description

Radiology reports cover different aspects, from radiological observation to the diagnosis of an imaging examination, such as X-rays, MRI, and CT scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision-making on patients’ health. To alleviate this issue, a novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified mogrifier LSTM model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1002/ima.23025
Additional Information:	Funding information: Guang Yang was supported in part by the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC\NSFC\211235), the NVIDIA Academic Hardware Grant Program, the SABER project supported by Boehringer Ingelheim Ltd, and the UKRI Future Leaders Fellowship (MR/V023799/1).
Data Access Statement:	Data sharing is not applicable to this article as no new data were created or analyzed in this study
Keywords:	clinical concept extraction, clinical name entity recognition, deep learning, Mogrifier LSTM, multihead attention, natural language processing
Departments, Centres and Research Units:	Computing
Dates:	Date Event 3 January 2024 Accepted 28 January 2024 Published Online January 2024 Published
Item ID:	34768
Date Deposited:	08 Feb 2024 11:30
Last Modified:	08 Feb 2024 15:36
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/34768

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