Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records

Chen Kai Wang, Feng Duo Wang, You Qian Lee, Pei Tsz Chen, Bo Hong Wang, Chu Hsien Su, Joseph Chin Chi Kuo, Chi Shin Wu, Yi Ling Chien, Hong Jie Dai, Vincent S. Tseng*, Wen Lian Hsu

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Code-mixing is a phenomenon where at least two languages are combined in a hybrid manner in the con of a single conversation. The use of mixed language is widespread in multilingual and multicultural countries and poses significant challenges for the development of automated language processing tools. In Taiwan's electronic health record (EHR) systems, unstructured EHR s are usually represented in a mixture of English and Chinese which increases the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applying several state-of-the-art pre-trained mono- and multilingual language models and propose to exploit the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus annotated with 6 main categories and 25 subcategories of PHIs. A hierarchical principle slot schema is defined in the PBA to encode knowledge of code-mixed PHIs and utilize slots to learn from the training set to assemble principles for recognizing PHI mentions and synthesizing surrogates simultaneously. In addition, a semantic disambiguation process is implemented to disambiguate ambiguous PHI categories in the de-identification process and to dynamically extend the knowledge encoded in PBA during the knowledge augmentation process. The experiment results demonstrate that the proposed method can achieve the best micro- and macro-F-scores in comparison to the other mono- and multilingual language models fine-tuned on our code-mixed corpus.

原文English
頁(從 - 到)22875-22885
頁數11
期刊IEEE Access
10
DOIs
出版狀態Published - 2022

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