Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts

Saadullah Amin, Noon Pokaratsiri Goldstein, Morgan Wixted, Alejandro Garcia-Rudolph, Catalina Martínez-Costa, Guenter Neumann


Abstract
Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured clinical texts. These texts, which often contain protected health information (PHI), are exposed to information extraction tools for downstream applications, risking patient identification. Existing works in de-identification rely on using large-scale annotated corpora in English, which often are not suitable in real-world multilingual settings. Pre-trained language models (LM) have shown great potential for cross-lingual transfer in low-resource settings. In this work, we empirically show the few-shot cross-lingual transfer property of LMs for named entity recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke domain. We annotate a gold evaluation dataset to assess few-shot setting performance where we only use a few hundred labeled examples for training. Our model improves the zero-shot F1-score from 73.7% to 91.2% on the gold evaluation set when adapting Multilingual BERT (mBERT) (CITATION) from the MEDDOCAN (CITATION) corpus with our few-shot cross-lingual target corpus. When generalized to an out-of-sample test set, the best model achieves a human-evaluation F1-score of 97.2%.
Anthology ID:
2022.bionlp-1.20
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
200–211
Language:
URL:
https://aclanthology.org/2022.bionlp-1.20
DOI:
10.18653/v1/2022.bionlp-1.20
Bibkey:
Cite (ACL):
Saadullah Amin, Noon Pokaratsiri Goldstein, Morgan Wixted, Alejandro Garcia-Rudolph, Catalina Martínez-Costa, and Guenter Neumann. 2022. Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 200–211, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts (Amin et al., BioNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bionlp-1.20.pdf
Code
 suamin/t2ner
Data
CoNLL 2002