Automated ICD Coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications

Zhao, Z; Lu, W; Peng, X; Xing, L; Zhang, W; Zheng, C

Automated ICD Coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications

Zhao, Z Lu, W Peng, X

Xing, L Zhang, W Zheng, C

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Consumer Electronics, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhao, Z
dc.contributor.author	Lu, W
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Xing, L
dc.contributor.author	Zhang, W
dc.contributor.author	Zheng, C
dc.date.accessioned	2024-09-05T10:54:08Z
dc.date.available	2024-09-05T10:54:08Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Consumer Electronics, 2024, PP, (99), pp. 1-1
dc.identifier.issn	0098-3063
dc.identifier.issn	1558-4127
dc.identifier.uri	http://hdl.handle.net/10453/180691
dc.description.abstract	Smart applications are essential in intelligent consumer electronics. With the rising focus on health concerns, self-diagnosis applications on smart devices have gained widespread popularity, attracting significant attention from both consumers and researchers. These applications play a crucial role in accurately predicting potential diseases based on user-provided symptom descriptions, thereby facilitating effective treatment. Distinguishing between diseases with similar symptoms yet requiring different treatments is essential. However, automated ICD coding faces challenges due to subtle differences between some ICD codes. Although existing methods has utilized contrastive learning to differentiate between codes, precise selection of high-quality negative samples and accurate matching of code descriptions with patient conditions remain critical factors influencing their performance. Drawing inspiration from medical practices, doctors tend to utilize back-reference knowledge to differentiate between similar diseases. We propose a novel framework for automated ICD coding via contrastive learning with back-reference and synonym knowledge (BRSK) for smart self-diagnosis applications. Specifically, by incorporating back-reference knowledge, we generate challenging negative samples, enhancing the effectiveness of contrastive learning. Additionally, multi-view synonym matching networks are used to fully leverage synonym knowledge, resulting in more precise modeling of semantic matching between codes and patient descriptions. Extensive experiments on MIMIC-III datasets demonstrate that BRSK significantly outperforms state-of-the-art baselines.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Consumer Electronics
dc.relation.isbasedon	10.1109/TCE.2024.3419447
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Automated ICD Coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-05T10:54:05Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Smart applications are essential in intelligent consumer electronics. With the rising focus on health concerns, self-diagnosis applications on smart devices have gained widespread popularity, attracting significant attention from both consumers and researchers. These applications play a crucial role in accurately predicting potential diseases based on user-provided symptom descriptions, thereby facilitating effective treatment. Distinguishing between diseases with similar symptoms yet requiring different treatments is essential. However, automated ICD coding faces challenges due to subtle differences between some ICD codes. Although existing methods has utilized contrastive learning to differentiate between codes, precise selection of high-quality negative samples and accurate matching of code descriptions with patient conditions remain critical factors influencing their performance. Drawing inspiration from medical practices, doctors tend to utilize back-reference knowledge to differentiate between similar diseases. We propose a novel framework for automated ICD coding via contrastive learning with back-reference and synonym knowledge (BRSK) for smart self-diagnosis applications. Specifically, by incorporating back-reference knowledge, we generate challenging negative samples, enhancing the effectiveness of contrastive learning. Additionally, multi-view synonym matching networks are used to fully leverage synonym knowledge, resulting in more precise modeling of semantic matching between codes and patient descriptions. Extensive experiments on MIMIC-III datasets demonstrate that BRSK significantly outperforms state-of-the-art baselines.

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http://hdl.handle.net/10453/180691