Published on Tue May 19 2020

Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain

Lukas Lange, Heike Adel, Jannik Strötgen

Exploiting natural language processing in the clinical domain requires anonymization of personal information in texts. Current research considers de-identification and downstream tasks, such as concept extraction, only in isolation. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data.

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Abstract

Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy-sensitive information and a multitask model. We set the new state of the art on benchmark datasets in English (96.1% F1 for de-identification and 88.9% F1 for concept extraction) and Spanish (91.4% F1 for concept extraction).