Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set

https://doi.org/10.1016/j.asoc.2020.106779Get rights and content

Highlights

  • Comparison of multilingual deep learning systems through clinical de-identification.

  • Proposal and testing of 4 possible training approaches with low resources languages.

  • Construction of a new annotated Italian dataset from public COVID-19 medical records.

Abstract

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.

Keywords

COVID-19
Clinical de-identification
Named entity recognition
Deep learning
Annotated Italian data set

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