Classification of Long COVID from general practitioner diagnosis text

Abbish Kamalakkannan; Gorkem Sezgin; Adam McLeod; Christopher Pearce; Andrew Georgiou; Mirela Prgomet; Stephen Weeding

doi:10.3233/SHTI230387

Classification of Long COVID from general practitioner diagnosis text

Abbish Kamalakkannan, Gorkem Sezgin, Adam McLeod, Christopher Pearce, Andrew Georgiou, Mirela Prgomet, Stephen Weeding

Australian Institute of Health Innovation

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

1 Citation (Scopus)

68 Downloads (Pure)

Abstract

Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.

Original language	English
Title of host publication	Context Sensitive Health Informatics and the Pandemic Boost
Subtitle of host publication	All Systems Go!
Editors	Adeola Bamgboje-Ayodele, Mirela Prgomet, Craig E. Kuziemsky, Peter Elkin, Christian Nøhr
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	124-125
Number of pages	2
ISBN (Electronic)	9781643683997
ISBN (Print)	9781643683980
DOIs	https://doi.org/10.3233/SHTI230387
Publication status	Published - 22 Jun 2023
Event	Context Sensitive Health Informatics Conference - The University of Sydney, Sydney, Australia Duration: 5 Jul 2023 → 6 Jul 2023 https://www.cshi-imia.org/

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	304
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	Context Sensitive Health Informatics Conference
Abbreviated title	CSHI 2023
Country/Territory	Australia
City	Sydney
Period	5/07/23 → 6/07/23
Internet address	https://www.cshi-imia.org/

Bibliographical note

Copyright the Author(s) and IOS Press 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

COVID-19
diagnosis text classification
long COVID
post COVID syndrome

Access to Document

10.3233/SHTI230387Licence: CC BY-NC

Publisher version (open access)Final published version, 143 KBLicence: CC BY-NC

Cite this

Kamalakkannan, A., Sezgin, G., McLeod, A., Pearce, C., Georgiou, A., Prgomet, M., & Weeding, S. (2023). Classification of Long COVID from general practitioner diagnosis text. In A. Bamgboje-Ayodele, M. Prgomet, C. E. Kuziemsky, P. Elkin, & C. Nøhr (Eds.), Context Sensitive Health Informatics and the Pandemic Boost: All Systems Go! (pp. 124-125). (Studies in Health Technology and Informatics; Vol. 304). IOS Press. https://doi.org/10.3233/SHTI230387

Kamalakkannan, Abbish ; Sezgin, Gorkem ; McLeod, Adam et al. / Classification of Long COVID from general practitioner diagnosis text. Context Sensitive Health Informatics and the Pandemic Boost: All Systems Go!. editor / Adeola Bamgboje-Ayodele ; Mirela Prgomet ; Craig E. Kuziemsky ; Peter Elkin ; Christian Nøhr. Amsterdam : IOS Press, 2023. pp. 124-125 (Studies in Health Technology and Informatics).

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abstract = "Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.",

keywords = "COVID-19, diagnosis text classification, long COVID, post COVID syndrome",

author = "Abbish Kamalakkannan and Gorkem Sezgin and Adam McLeod and Christopher Pearce and Andrew Georgiou and Mirela Prgomet and Stephen Weeding",

note = "Copyright the Author(s) and IOS Press 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; Context Sensitive Health Informatics Conference, CSHI 2023 ; Conference date: 05-07-2023 Through 06-07-2023",

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Kamalakkannan, A, Sezgin, G, McLeod, A, Pearce, C, Georgiou, A , Prgomet, M & Weeding, S 2023, Classification of Long COVID from general practitioner diagnosis text. in A Bamgboje-Ayodele, M Prgomet, CE Kuziemsky, P Elkin & C Nøhr (eds), Context Sensitive Health Informatics and the Pandemic Boost: All Systems Go!. Studies in Health Technology and Informatics, vol. 304, IOS Press, Amsterdam, pp. 124-125, Context Sensitive Health Informatics Conference, Sydney, Australia, 5/07/23. https://doi.org/10.3233/SHTI230387

Classification of Long COVID from general practitioner diagnosis text. / Kamalakkannan, Abbish; Sezgin, Gorkem; McLeod, Adam et al.
Context Sensitive Health Informatics and the Pandemic Boost: All Systems Go!. ed. / Adeola Bamgboje-Ayodele; Mirela Prgomet; Craig E. Kuziemsky; Peter Elkin; Christian Nøhr. Amsterdam: IOS Press, 2023. p. 124-125 (Studies in Health Technology and Informatics; Vol. 304).

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

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T1 - Classification of Long COVID from general practitioner diagnosis text

AU - Kamalakkannan, Abbish

AU - Sezgin, Gorkem

AU - McLeod, Adam

AU - Pearce, Christopher

AU - Georgiou, Andrew

AU - Prgomet, Mirela

AU - Weeding, Stephen

N1 - Copyright the Author(s) and IOS Press 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2023/6/22

Y1 - 2023/6/22

N2 - Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.

AB - Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.

KW - COVID-19

KW - diagnosis text classification

KW - long COVID

KW - post COVID syndrome

UR - https://www.scopus.com/pages/publications/85162779071

U2 - 10.3233/SHTI230387

DO - 10.3233/SHTI230387

M3 - Conference proceeding contribution

C2 - 37347585

SN - 9781643683980

T3 - Studies in Health Technology and Informatics

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BT - Context Sensitive Health Informatics and the Pandemic Boost

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A2 - Prgomet, Mirela

A2 - Kuziemsky, Craig E.

A2 - Elkin, Peter

A2 - Nøhr, Christian

PB - IOS Press

CY - Amsterdam

T2 - Context Sensitive Health Informatics Conference

Y2 - 5 July 2023 through 6 July 2023

ER -

Kamalakkannan A, Sezgin G, McLeod A, Pearce C, Georgiou A , Prgomet M et al. Classification of Long COVID from general practitioner diagnosis text. In Bamgboje-Ayodele A, Prgomet M, Kuziemsky CE, Elkin P, Nøhr C, editors, Context Sensitive Health Informatics and the Pandemic Boost: All Systems Go!. Amsterdam: IOS Press. 2023. p. 124-125. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230387

Classification of Long COVID from general practitioner diagnosis text

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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