Abstract
The huge volume of textual information generated in hospitals constitutes an essential but underused asset that could be exploited to improve patient care and management. The encoding of raw medical texts into fixed data structures is traditionally addressed with knowledge-based models and complex hand-crafted rules, but the rigidity of this approach poses limitations to the generalizability and transferability of the solutions, in particular for a non-English setting under data scarcity conditions. This paper shows that transformer-based language representation models have the right characteristics to be employed as a more flexible but equally high-performing clinical information retrieval system for this scenario, without relying upon a knowledge-driven component. We demonstrate it pragmatically on the extraction of clinical entities from Italian cardiology reports for patients with inherited arrhythmias, outperforming the previous ontology-based work with our proposed transformer pipeline under the same setting and exploring a new rule-free approach based on question answering to automate cardiological registry filling.
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Notes
- 1.
Source code available at https://github.com/detsutut/icsm-cardio-nlp.
- 2.
The metathesaurus statistics for different languages over the years can be found at https://www.nlm.nih.gov/research/umls/archive/archive_home.html.
- 3.
Model repository: https://huggingface.co/dbmdz/bert-base-italian-xxl-cased.
References
Aronson, A.R., Lang, F.M.: An overview of MetaMap: historical perspective and recent advances. J. Am. Med. Inform. Assoc. JAMIA 17(3), 229–236 (2010). https://doi.org/10.1136/jamia.2009.002733
Buonocore, T.M., Crema, C., Redolfi, A., Bellazzi, R., Parimbelli, E.: Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models, December 2022. https://doi.org/10.48550/arXiv.2212.10422, http://arxiv.org/abs/2212.10422, arXiv:2212.10422 [cs]
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: 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, June 2019. https://doi.org/10.18653/v1/N19-1423, https://www.aclweb.org/anthology/N19-1423
Mulyar, A., Uzuner, O., McInnes, B.: MT-clinical BERT: scaling clinical information extraction with multitask learning. J. Am. Med. Inform. Associ. 28(10), 2108–2115 (2021). https://doi.org/10.1093/jamia/ocab126, https://doi.org/10.1093/jamia/ocab126
Percha, B., Pisapati, K., Gao, C., Schmidt, H.: Natural language inference for curation of structured clinical registries from unstructured text. J. Am. Med. Inform. Assoc. 29(1), 97–108 (2022). https://doi.org/10.1093/jamia/ocab243
Savova, G.K., et al.: Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. JAMIA 17(5), 507–513 (2010). https://doi.org/10.1136/jamia.2009.001560, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995668/
Viani, N., et al.: Information extraction from Italian medical reports: an ontology-driven approach. Int. J. Med. Inform. 111 (2017). https://doi.org/10.1016/j.ijmedinf.2017.12.013
Viani, N., et al.: Supervised methods to extract clinical events from cardiology reports in Italian. J. Biomed. Inform. 95, 103219 (2019). https://doi.org/10.1016/j.jbi.2019.103219
Viviani, L., Zolin, A., Mehta, A., Olesen, H.V.: The European cystic fibrosis society patient registry: valuable lessons learned on how to sustain a disease registry. Orphanet J. Rare Diseases 9(1), 81 (2014). https://doi.org/10.1186/1750-1172-9-81
Wang, Y., et al.: Clinical information extraction applications: a literature review. J. Biomed. Inform. 77, 34–49 (2018). https://doi.org/10.1016/j.jbi.2017.11.011, https://www.sciencedirect.com/science/article/pii/S1532046417302563
Wei, Q., et al.: Relation extraction from clinical narratives using pre-trained language models. In: AMIA Annual Symposium Proceedings 2019, pp. 1236–1245, March 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153059/
Yang, X., Bian, J., Hogan, W.R., Wu, Y.: Clinical concept extraction using transformers. J. Am. Med. Inform. Assoc. 27(12), 1935–1942 (2020). https://doi.org/10.1093/jamia/ocaa189
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Buonocore, T.M., Parimbelli, E., Tibollo, V., Napolitano, C., Priori, S., Bellazzi, R. (2023). A Rule-Free Approach for Cardiological Registry Filling from Italian Clinical Notes with Question Answering Transformers. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_19
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