Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References

Vasudevan Nedumpozhimana; Sneha Rautmare; Meegan Gower; Maja Popovic; Nishtha Jain; Patricia Buffini; John Kelleher

doi:10.26615/978-954-452-092-2_084

Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References

Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

School of Computer Science

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

Abstract

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Original language	English
Title of host publication	International Conference Recent Advances in Natural Language Processing, RANLP 2023
Subtitle of host publication	Large Language Models for Natural Language Processing - Proceedings
Editors	Galia Angelova, Maria Kunilovskaya, Ruslan Mitkov
Publisher	Incoma Ltd
Pages	777-784
Number of pages	8
ISBN (Electronic)	9789544520922
DOIs	https://doi.org/10.26615/978-954-452-092-2_084
Publication status	Published - 2023
Event	2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023 - Varna, Bulgaria Duration: 4 Sep 2023 → 6 Sep 2023

Publication series

Name	International Conference Recent Advances in Natural Language Processing, RANLP
ISSN (Print)	1313-8502

Conference

Conference	2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023
Country/Territory	Bulgaria
City	Varna
Period	4/09/23 → 6/09/23

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10.26615/978-954-452-092-2_084

https://arrow.tudublin.ie/diraacon/19Licence: CC BY-NC-SA

Cite this

Nedumpozhimana, V., Rautmare, S., Gower, M., Popovic, M., Jain, N., Buffini, P., & Kelleher, J. (2023). Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References. In G. Angelova, M. Kunilovskaya, & R. Mitkov (Eds.), International Conference Recent Advances in Natural Language Processing, RANLP 2023: Large Language Models for Natural Language Processing - Proceedings (pp. 777-784). (International Conference Recent Advances in Natural Language Processing, RANLP). Incoma Ltd. https://doi.org/10.26615/978-954-452-092-2_084

Nedumpozhimana, Vasudevan ; Rautmare, Sneha ; Gower, Meegan et al. / Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References. International Conference Recent Advances in Natural Language Processing, RANLP 2023: Large Language Models for Natural Language Processing - Proceedings. editor / Galia Angelova ; Maria Kunilovskaya ; Ruslan Mitkov. Incoma Ltd, 2023. pp. 777-784 (International Conference Recent Advances in Natural Language Processing, RANLP).

@inproceedings{0a20c91509b24dac9d9071be35fe3964,

title = "Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References",

abstract = "Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.",

author = "Vasudevan Nedumpozhimana and Sneha Rautmare and Meegan Gower and Maja Popovic and Nishtha Jain and Patricia Buffini and John Kelleher",

note = "Publisher Copyright: {\textcopyright} 2023 Incoma Ltd. All rights reserved.; 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023 ; Conference date: 04-09-2023 Through 06-09-2023",

year = "2023",

doi = "10.26615/978-954-452-092-2_084",

language = "English",

series = "International Conference Recent Advances in Natural Language Processing, RANLP",

publisher = "Incoma Ltd",

pages = "777--784",

editor = "Galia Angelova and Maria Kunilovskaya and Ruslan Mitkov",

booktitle = "International Conference Recent Advances in Natural Language Processing, RANLP 2023",

}

Nedumpozhimana, V, Rautmare, S, Gower, M, Popovic, M, Jain, N, Buffini, P & Kelleher, J 2023, Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References. in G Angelova, M Kunilovskaya & R Mitkov (eds), International Conference Recent Advances in Natural Language Processing, RANLP 2023: Large Language Models for Natural Language Processing - Proceedings. International Conference Recent Advances in Natural Language Processing, RANLP, Incoma Ltd, pp. 777-784, 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023, Varna, Bulgaria, 4/09/23. https://doi.org/10.26615/978-954-452-092-2_084

Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References. / Nedumpozhimana, Vasudevan; Rautmare, Sneha; Gower, Meegan et al.
International Conference Recent Advances in Natural Language Processing, RANLP 2023: Large Language Models for Natural Language Processing - Proceedings. ed. / Galia Angelova; Maria Kunilovskaya; Ruslan Mitkov. Incoma Ltd, 2023. p. 777-784 (International Conference Recent Advances in Natural Language Processing, RANLP).

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

TY - GEN

T1 - Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References

AU - Nedumpozhimana, Vasudevan

AU - Rautmare, Sneha

AU - Gower, Meegan

AU - Popovic, Maja

AU - Jain, Nishtha

AU - Buffini, Patricia

AU - Kelleher, John

PY - 2023

Y1 - 2023

N2 - Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

AB - Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

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U2 - 10.26615/978-954-452-092-2_084

DO - 10.26615/978-954-452-092-2_084

M3 - Conference contribution

AN - SCOPUS:85179184096

T3 - International Conference Recent Advances in Natural Language Processing, RANLP

SP - 777

EP - 784

BT - International Conference Recent Advances in Natural Language Processing, RANLP 2023

A2 - Angelova, Galia

A2 - Kunilovskaya, Maria

A2 - Mitkov, Ruslan

PB - Incoma Ltd

T2 - 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

Y2 - 4 September 2023 through 6 September 2023

ER -

Nedumpozhimana V, Rautmare S, Gower M, Popovic M, Jain N, Buffini P et al. Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References. In Angelova G, Kunilovskaya M, Mitkov R, editors, International Conference Recent Advances in Natural Language Processing, RANLP 2023: Large Language Models for Natural Language Processing - Proceedings. Incoma Ltd. 2023. p. 777-784. (International Conference Recent Advances in Natural Language Processing, RANLP). doi: 10.26615/978-954-452-092-2_084

Medical Concept Mention Identification in Social Media Posts using a Small Number of Sample References

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