Skip to main content

Medical Entity Linking in Laypersons’ Language

  • Conference paper
  • First Online:
Book cover Advances in Information Retrieval (ECIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

Included in the following conference series:

  • 2442 Accesses

Abstract

Due to the vast amount of health-related data on the Internet, a trend toward digital health literacy is emerging among laypersons. We hypothesize that providing trustworthy explanations of informal medical terms in social media can improve information quality. Entity linking (EL) is the task of associating terms with concepts (entities) in the knowledge base. The challenge with EL in lay medical texts is that the source texts are often written in loose and informal language. We propose an end-to-end entity linking approach that involves identifying informal medical terms, normalizing medical concepts according to SNOMED-CT, and linking entities to Wikipedia to provide explanations for laypersons.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The paper was presented in Forum for Information Retrieval Evaluation (FIRE) 2021 conference.

References

  1. Basaldella, M., Liu, F., Shareghi, E., Collier, N.: COMETA: a corpus for medical entity linking in the social media. In: Proceedings of the 2020 Conference on EMNLP, pp. 3122–3137. ACL, November 2020

    Google Scholar 

  2. Donnelly, K.: Snomed-ct: the advanced terminology and coding system for ehealth. Stud. Health Technol. Inf. 121, 279–290 (2006)

    Google Scholar 

  3. Eurobarometer: European citizens’ digital health literacy. A report to the European Commission (2014)

    Google Scholar 

  4. Fage-Butler, A.M., Nisbeth Jensen, M.: Medical terminology in online patient-patient communication: evidence of high health literacy? Health Expect. 19(3), 643–653 (2016)

    Article  Google Scholar 

  5. Fage-Butler, A.M., Jensen, M.N.: The interpersonal dimension of online patient forums: How patients manage informational and relational aspects in response to posted questions. HERMES-J. Lang. Commun. Bus. 51, 21–38 (2013)

    Google Scholar 

  6. Hedderich, M.A., Lange, L., Adel, H., Strötgen, J., Klakow, D.: A survey on recent approaches for natural language processing in low-resource scenarios. arXiv preprint arXiv:2010.12309 (2020)

  7. Karimi, S., Metke-Jimenez, A., Kemp, M., Wang, C.: Cadec: a corpus of adverse drug event annotations. J. Biomed. Inform. 55, 73–81 (2015)

    Article  Google Scholar 

  8. Kolitsas, N., Ganea, O.E., Hofmann, T.: End-to-end neural entity linking. In: Proceedings of the 22nd Conference on CoNLL, pp. 519–529. ACL, Brussels, Belgium, October 2018

    Google Scholar 

  9. Limsopatham, N., Collier, N.: Adapting phrase-based machine translation to normalise medical terms in social media messages. In: Proceedings of the 2015 Conference on EMNLP, pp. 1675–1680. ACL, Lisbon, Portugal, September 2015

    Google Scholar 

  10. Limsopatham, N., Collier, N.: Normalising medical concepts in social media texts by learning semantic representation. In: Proceedings of the 54th Annual Meeting of the ACL, pp. 1014–1023. ACL, Berlin, Germany, August 2016

    Google Scholar 

  11. Miftahutdinov, Z., Tutubalina, E.: Deep neural models for medical concept normalization in user-generated texts. In: Proceedings of the 57th Annual Meeting of the ACL: Student Research Workshop, pp. 393–399. ACL, Florence, Italy, July 2019

    Google Scholar 

  12. Pattisapu, N., Anand, V., Patil, S., Palshikar, G., Varma, V.: Distant supervision for medical concept normalization. J. Biomed. Inform. 109, 103522 (2020)

    Article  Google Scholar 

  13. Polepalli Ramesh, B., Houston, T., Brandt, C., Fang, H., Yu, H.: Improving patients’ electronic health record comprehension with noteaid. In: MEDINFO 2013, pp. 714–718. IOS Press (2013)

    Google Scholar 

  14. Scepanovic, S., Martin-Lopez, E., Quercia, D., Baykaner, K.: Extracting medical entities from social media. In: Proceedings of the ACM Conference on Health, Inference, and Learning, CHIL 2020, pp. 170–181. ACM, New York (2020)

    Google Scholar 

  15. Seiffe, L., Marten, O., Mikhailov, M., Schmeier, S., Möller, S., Roller, R.: From witch’s shot to music making bones - resources for medical laymen to technical language and vice versa. In: Proceedings of the 12th LREC, pp. 6185–6192. ELRA, Marseille, France, May 2020

    Google Scholar 

  16. Tutubalina, E., Miftahutdinov, Z., Nikolenko, S., Malykh, V.: Medical concept normalization in social media posts with recurrent neural networks. J. Biomed. Inform. 84, 93–102 (2018)

    Article  Google Scholar 

  17. Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: Proceedings of the 2019 Conference on EMNLP and the 9th EMNLP-IJCNLP. pp. 6382–6388. ACL, Hong Kong, China, November 2019

    Google Scholar 

  18. Zolnoori, M., et al.: The psytar dataset: from patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications. Data Brief 24, 103838 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Annisa Maulida Ningtyas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ningtyas, A.M. (2022). Medical Entity Linking in Laypersons’ Language. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-99739-7_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99738-0

  • Online ISBN: 978-3-030-99739-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics