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Generating features for named entity recognition by learning prototypes in semantic space: The case of de-identifying health records.

, , and . BIBM, page 450-457. IEEE Computer Society, (2014)

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Clinical Text Mining - Secondary Use of Electronic Patient Records. Springer, (2018)De-identifying Swedish clinical text - refinement of a gold standard and experiments with Conditional random fields., and . J. Biomed. Semant., (2010)Knowledge patterns for online health portal development., , and . Health Informatics J., (2019)Evaluating a Spelling Support in a Search Engine.. NLDB, volume 2553 of Lecture Notes in Computer Science, page 183-190. Springer, (2002)Implementation of specialised attention mechanisms: ICD-10 classification of Gastrointestinal discharge summaries in English, Spanish and Swedish., , , , and . J. Biomed. Informatics, (2022)The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text., , and . LOUHI@EMNLP, page 1-11. Association for Computational Linguistics, (2020)Vocabulary Modifications for Domain-adaptive Pretraining of Clinical Language Models., , and . HEALTHINF, page 180-188. SCITEPRESS, (2022)Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection., , , and . DSAA, page 1-8. IEEE, (2015)HB Deid - HB De-identification tool demonstrator., and . NoDaLiDa, page 467-471. Linköping University Electronic Press, Sweden, (2021)Improving search engine retrieval using a compound splitter for Swedish.. NODALIDA, page 38-42. University of Joensuu, Finland, (2005)