https://scholars.lib.ntu.edu.tw/handle/123456789/627053
標題: | Principle-Based Approach for the De-Identification of Code-Mixed Electronic Health Records | 作者: | Wang, CK Wang, FD Lee, YQ Chen, PT Wang, BH Su, CH Kuo, JCC CHI-SHIN WU YI-LING CHIEN Dai, HJ Tseng, VS Hsu, WL |
關鍵字: | Hospitals; Training; Electronic medical records; Task analysis; Semantics; Knowledge engineering; Electrical engineering; Electronic health record; data anonymization; code-mixing; principle; named entity recognition; deep learning | 公開日期: | 2022 | 出版社: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 卷: | 10 | 起(迄)頁: | 22875 | 來源出版物: | IEEE ACCESS | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/627053 | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2022.3148396 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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