Negation Detection in Medical Texts
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63772-8_6
Reference19 articles.
1. Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G.: A simple algorithm for identifying negated findings and diseases in discharge summaries. J. Biomed. Inform. 34(5), 301–310 (2001)
2. Dalloux, C., Claveau, V., Grabar, N.: Speculation and negation detection in French biomedical corpora. In: Mitkov, R., Angelova, G. (eds.) Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 223–232. INCOMA Ltd., Varna, Bulgaria (2019). https://doi.org/10.26615/978-954-452-056-4_026, https://aclanthology.org/R19-1026
3. Elkin, P.L., et al.: A controlled trial of automated classification of negation from clinical notes. BMC Med. Inform. Decis. Mak. 5, 1–7 (2005)
4. van Es, B., et al.: Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods. BMC Bioinform. 24(1), 10 (2023)
5. Funkner, A., Balabaeva, K., Kovalchuk, S.: Negation detection for clinical text mining in Russian. Stud. Health Technol. Inform. 270, 342–346 (2020). https://doi.org/10.3233/SHTI200179
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