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.
Keywords
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The paper was presented in Forum for Information Retrieval Evaluation (FIRE) 2021 conference.
References
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
Donnelly, K.: Snomed-ct: the advanced terminology and coding system for ehealth. Stud. Health Technol. Inf. 121, 279–290 (2006)
Eurobarometer: European citizens’ digital health literacy. A report to the European Commission (2014)
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)
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)
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)
Karimi, S., Metke-Jimenez, A., Kemp, M., Wang, C.: Cadec: a corpus of adverse drug event annotations. J. Biomed. Inform. 55, 73–81 (2015)
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
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
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
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
Pattisapu, N., Anand, V., Patil, S., Palshikar, G., Varma, V.: Distant supervision for medical concept normalization. J. Biomed. Inform. 109, 103522 (2020)
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)
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)
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
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)
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
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)
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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
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DOI: https://doi.org/10.1007/978-3-030-99739-7_63
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