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Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set
Applied Soft Computing Journal Số , năm 2020 (Tập 97, trang -)
DOI: 10.1016/j.asoc.2020.106779
Tài liệu thuộc danh mục: ISI, Scopus
English
English
Từ khóa: Deep learning; Records management; Transfer learning; Viruses; Clinical records; Cross-lingual; De-identification; Learning techniques; Low resource languages; Medical record; Named entity recognition; Protected health informations; Learning systems
Tóm tắt tiếng anh
The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets. Hence a new Italian de-identification data set has been created from the COVID-19 clinical records made available by the Italian Society of Radiology (SIRM). Therefore, two multi-lingual deep learning systems have been developed for this low-resource language scenario: the objective is to investigate their ability to transfer knowledge between different languages while maintaining the necessary features to correctly perform the Named Entity Recognition task for de-identification. The systems were trained using four different strategies, using both the English Informatics for Integrating Biology & the Bedside (i2b2) 2014 and the new Italian SIRM COVID-19 data sets, then evaluated on the latter. These approaches have demonstrated the effectiveness of cross-lingual transfer learning to de-identify medical records written in a low resource language such as Italian, using one with high resources such as English. � 2020 Elsevier B.V.
