A New Semantic Similarity Scheme for more Accurate Identification in Medical Data

Colin Wilcox; Soufiene Djahel; Vasilios Giagos; Kristopher Welsh; Nicholas Costen

doi:10.1109/ISC257844.2023.10293563

A New Semantic Similarity Scheme for more Accurate Identification in Medical Data

Colin Wilcox, Soufiene Djahel, Vasilios Giagos, Kristopher Welsh, Nicholas Costen

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

This paper aims to design a new measure of similarity between personal textual information retrieved from historic medical records to correct errors introduced due to poor encoding and data omission. The key motivation underlying our proposed layered algorithm, named Semantic Similarity scheme (SSIM), is to create a consistent, complete and accurate data set that may then be used as a basis for the identification and authentication of individuals in a medical context. Such consistent data may provide a basis for use as part of an access control system without compromising medical ethics or security. The obtained evaluation results, using four sample data sets from the UK, USA, Canada and Australia, highlight promising benefits compared to other similarity measures including Jaccard index, Sorensen-Dice and Cosine Similarity - especially when nicknames, abbreviations and synonyms are used to determine similarity.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-7
Number of pages	7
ISBN (Electronic)	9798350397758
ISBN (Print)	9798350397758, 9798350397741, 9798350397765
DOIs	https://doi.org/10.1109/ISC257844.2023.10293563
Publication status	Published - 31 Oct 2023
Externally published	Yes
Event	IEEE International Smart Cities Conference (ISC2) - Bucharest, Romania Duration: 24 Sept 2023 → 27 Sept 2023

Publication series

Name	Proceedings of the IEEE International Smart Cities Conference
Publisher	IEEE
ISSN (Print)	2687-8852
ISSN (Electronic)	2687-8860

Conference

Conference	IEEE International Smart Cities Conference (ISC2)
Country/Territory	Romania
City	Bucharest
Period	24/09/23 → 27/09/23

Keywords

Medical Data
Medical Records
Semantic Similarity
Text Similarity

ASJC Scopus subject areas

Artificial Intelligence
Communication
Energy Engineering and Power Technology
Computer Vision and Pattern Recognition
Computer Science Applications
Renewable Energy, Sustainability and the Environment
Urban Studies

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISC257844.2023.10293563

Cite this

Wilcox, C., Djahel, S., Giagos, V., Welsh, K., & Costen, N. (2023). A New Semantic Similarity Scheme for more Accurate Identification in Medical Data. In Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023 (pp. 1-7). (Proceedings of the IEEE International Smart Cities Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISC257844.2023.10293563

A New Semantic Similarity Scheme for more Accurate Identification in Medical Data. / Wilcox, Colin; Djahel, Soufiene; Giagos, Vasilios et al.
Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-7 (Proceedings of the IEEE International Smart Cities Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Wilcox, C, Djahel, S, Giagos, V, Welsh, K & Costen, N 2023, A New Semantic Similarity Scheme for more Accurate Identification in Medical Data. in Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023. Proceedings of the IEEE International Smart Cities Conference, Institute of Electrical and Electronics Engineers Inc., pp. 1-7, IEEE International Smart Cities Conference (ISC2), Bucharest, Romania, 24/09/23. https://doi.org/10.1109/ISC257844.2023.10293563

Wilcox C, Djahel S, Giagos V, Welsh K, Costen N. A New Semantic Similarity Scheme for more Accurate Identification in Medical Data. In Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1-7. (Proceedings of the IEEE International Smart Cities Conference). doi: 10.1109/ISC257844.2023.10293563

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abstract = "This paper aims to design a new measure of similarity between personal textual information retrieved from historic medical records to correct errors introduced due to poor encoding and data omission. The key motivation underlying our proposed layered algorithm, named Semantic Similarity scheme (SSIM), is to create a consistent, complete and accurate data set that may then be used as a basis for the identification and authentication of individuals in a medical context. Such consistent data may provide a basis for use as part of an access control system without compromising medical ethics or security. The obtained evaluation results, using four sample data sets from the UK, USA, Canada and Australia, highlight promising benefits compared to other similarity measures including Jaccard index, Sorensen-Dice and Cosine Similarity - especially when nicknames, abbreviations and synonyms are used to determine similarity.",

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A New Semantic Similarity Scheme for more Accurate Identification in Medical Data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

A Novel Framework for Secure and Privacy-Preserving Access to Healthcare Resources

Cite this

A New Semantic Similarity Scheme for more Accurate Identification in Medical Data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

A Novel Framework for Secure and Privacy-Preserving Access to Healthcare Resources

Cite this