Electronic health records (EHRs) at medical institutions provide valuable sources for research in both clinical and biomedical domains. However, before such records can be used for research purposes, protected health information (PHI) mentioned in the unstructured text must be removed. In Taiwan’s EHR systems the unstructured EHR texts are usually represented in the mixing of English and Chinese languages, which brings challenges for de-identification. This paper presented the first study, to the best of our knowledge, of the construction of a code-mixed EHR de-identification corpus and the evaluation of different mature entity recognition methods applied for the code-mixed PHI recognition task.
Code-mixing is a phenomenon when at least two languages combined in a hybrid way in the context 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, the unstructured EHR texts are usually represented in the mixing of English and Chinese languages resulting in the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applied several state-of-the-art pretrained mono-and multilingual language models and proposed to apply the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus, which was annotated with 6 main categories and 25 subcategories of PHIs. In PBA, a hierarchical principle slot schema is defined to encode knowledge of code-mixed PHIs and the defined slots were learned from the training set to assemble into principles for recognizing PHI mentions and synthesizing surrogates at the same time. A semantic disambiguation process is developed used 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 experimental results demonstrate that the proposed method can achieve the best micro-and macro-F-scores performance in comparison with the other mono-and multilingual language models finetuned on our code-mixed corpus.
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