NeighBERT: Medical Entity Linking Using Relation-Induced Dense Retrieval
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Science Applications,Health Informatics,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s41666-023-00136-3.pdf
Reference54 articles.
1. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota. https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423. Accessed 10 Jan 2024
2. Kraljevic Z, Searle T, Shek A, Roguski L, Noor K, Bean D, Mascio A, Zhu L, Folarin AA, Roberts A, Bendayan R, Richardson MP, Stewart R, Shah AD, Wong WK, Ibrahim Z, Teo JT, Dobson RJB (2021) Multi-domain clinical natural language processing with MedCAT: The medical concept annotation toolkit. Artif Intell Med 117:102083. https://doi.org/10.1016/j.artmed.2021.102083
3. Soldaini L, Goharian N (2016) QuickUMLS: a fast, unsupervised approach for medical concept extraction. In: MedIR Workshop, Sigir, pp. 1–4
4. Mohan S, Angell R, Monath N, McCallum A (2021) Low resource recognition and linking of biomedical concepts from a large ontology. In: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 1–10. https://doi.org/10.1145/3459930.3469524
5. Newman-Griffis D, Divita G, Desmet B, Zirikly A, Rose CP, Fosler-Lussier E (2021) Ambiguity in medical concept normalization: an analysis of types and coverage in electronic health record datasets. J Am Med Inform Assoc 28(3):516–532
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