Abstract
Biomedical entity linking is an essential building block for various clinical applications and downstream NLP tasks. However, only few annotated biomedical datasets with grounded entity mentions for non-English languages are available for training supervised machine learning models. Moreover, the majority of concept aliases in medical vocabularies are also only available in English.
In this work, we consider the problem of linking disease mentions in Spanish clinical case reports to concept identifiers in SNOMED CT, a comprehensive medical terminology system. For these concepts, only a limited number of aliases in the source language are given, but many more can be obtained from other languages and medical vocabularies. We propose a system that utilizes these multilingual aliases to retrieve candidate concepts for a given entity mention and re-ranks retrieved candidates using a trainable cross-encoder. We evaluate our system on the DisTEMIST shared task dataset of the 10th BioASQ challenge.
Our results show that supervised re-ranking outperforms the previously best-performing rule-based system, while requiring much less task-specific hyperparameter tuning. Detailed ablation experiments demonstrate that multilingual aliases are highly beneficial to improve recall during candidate generation, but hardly affect re-ranking performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, D., Angell, R., Monath, N., McCallum, A.: Entity linking via explicit mention-mention coreference modeling. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle (2022)
Alekseev, A., Miftahutdinov, Z., Tutubalina, E., et al.: Medical crossing: a cross-lingual evaluation of clinical entity linking. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 4212–4220. European Language Resources Association, Marseille (2022)
Bernik, M., Tovornika, R., Fabjana, B., Marco-Ruizb, L.: Diagñoza: a natural language processing tool for automatic annotation of clinical free text with SNOMED-CT. In: Working Notes of CLEF. CEUR Workshop Proceedings, pp. 235–243 (2022)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–D270 (2004)
Borchert, F., Lohr, C., Modersohn, L., et al.: GGPONC 2.0-the German clinical guideline corpus for oncology: Curation workflow, annotation policy, baseline NER taggers. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3650–3660 (2022)
Borchert, F., Schapranow, M.P.: HPI-DHC @ BioASQ DisTEMIST: Spanish biomedical entity linking with pre-trained Transformers and cross-lingual candidate retrieval. In: Working Notes of CLEF. CEUR Workshop Proceedings, pp. 244–258. Italy, Bologna (2022)
Carrino, C.P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., et al.: Biomedical and clinical language models for Spanish: on the benefits of domain-specific pretraining in a mid-resource scenario. arXiv preprint arXiv:2109.03570 (2021)
Chizhikova, M., Collado-Montañez, J., López-Úbeda, P., et al.: SINAI at CLEF 2022: leveraging biomedical transformers to detect and normalize disease mentions. In: Working Notes of CLEF. CEUR Workshop Proceedings, pp. 265–273 (2022)
Donnelly, K.: SNOMED-CT: the advanced terminology and coding system for eHealth. In: Medical and Care Compunetics 3. No. 121 in Studies in Health Technology and Informatics, pp. 279–290. IOS Press (2006)
Fries, J., Weber, L., Seelam, N., et al.: BigBIO: a framework for data-centric biomedical natural language processing. In: Advances in Neural Information Processing Systems, vol. 35, pp. 25792–25806. Curran Associates, Inc. (2022)
HPI Digital Health Cluster on GitHub: xMEN (2023). https://github.com/hpi-dhc/xmen. Accessed 23 June 2023
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535–547 (2019)
Liu, F., Vulić, I., Korhonen, A., Collier, N.: Learning domain-specialised representations for cross-lingual biomedical entity linking. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 565–574. Association for Computational Linguistics, Online (2021)
Liu, Y., Ott, M., Goyal, N., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Logeswaran, L., Chang, M.W., Lee, K., et al.: Zero-shot entity linking by reading entity descriptions. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3449–3460. Association for Computational Linguistics, Florence (2019)
Miranda-Escalada, A., Gascó, L., Lima-López, S., et al.: Overview of DisTEMIST at BioASQ: automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. In: Working Notes of CLEF. CEUR Workshop Proceedings (2022)
Mohan, S., Li, D.: MedMentions: a large biomedical corpus annotated with UMLS concepts. In: Proceedings of the 2019 Conference on Automated Knowledge Base Construction, Amherst, Massachusetts, USA (2019)
Neumann, M., King, D., Beltagy, I., Ammar, W.: scispaCy: fast and robust models for biomedical natural language processing. In: Proceedings of the 18th BioNLP Workshop and Shared Task, pp. 319–327. Association for Computational Linguistics, Florence (2019)
Névéol, A., Cohen, K.B., Grouin, C., et al.: Clinical information extraction at the CLEF eHealth evaluation lab 2016. In: CEUR Workshop Proceedings, vol. 1609, p. 28 (2016)
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 3982–3992. Association for Computational Linguistics, Hong Kong (2019)
Robertson, S., Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc. (2009)
Roller, R., Kittner, M., Weissenborn, D., Leser, U.: Cross-lingual candidate search for biomedical concept normalization. In: MultilingualBIO: Multilingual Biomedical Text Processing, p. 16 (2018)
Roller, R., Uszkoreit, H., Xu, F., et al.: A fine-grained corpus annotation schema of German nephrology records. In: Proceedings of the Clinical Natural Language Processing Workshop, Osaka, Japan, pp. 69–77 (2016)
Sevgili, Ö., Shelmanov, A., Arkhipov, M., et al.: Neural entity linking: a survey of models based on deep learning. Semant. Web 13(3), 527–570 (2022)
Sung, M., Jeon, H., Lee, J., Kang, J.: Biomedical entity representations with synonym marginalization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3641–3650. Association for Computational Linguistics, Online (2020)
Vashishth, S., Newman-Griffis, D., Joshi, R., et al.: Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets. J. Biomed. Inform. 121, 103880 (2021)
Wajsbürt, P., Sarfati, A., Tannier, X.: Medical concept normalization in French using multilingual terminologies and contextual embeddings. J. Biomed. Inform. 114, 103684 (2021)
Wu, L., Petroni, F., Josifoski, M., et al.: Scalable zero-shot entity linking with dense entity retrieval. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 6397–6407. Association for Computational Linguistics, Online (2020)
Xu, D., Zhang, Z., Bethard, S.: A generate-and-rank framework with semantic type regularization for biomedical concept normalization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8452–8464. Association for Computational Linguistics, Online (2020)
Acknowledgment
Parts of this work were generously supported by grants of the German Federal Ministry of Research and Education (01ZZ1802H, 01ZZ2314N) and the German Federal Ministry of Economic Affairs and Climate Action (01MJ21002A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Borchert, F., Llorca, I., Schapranow, MP. (2023). Cross-Lingual Candidate Retrieval and Re-ranking for Biomedical Entity Linking. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-42448-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42447-2
Online ISBN: 978-3-031-42448-9
eBook Packages: Computer ScienceComputer Science (R0)