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Exploiting natural language processing in the clinical domain requiresde-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 ofde-identification on other tasks. In this paper, we close this gap by reportingconcept extraction performance on automatically anonymized data andinvestigating joint models for de-identification and concept extraction. Inparticular, we propose a stacked model with restricted access toprivacy-sensitive information and a multitask model. We set the new state ofthe art on benchmark datasets in English (96.1% F1 for de-identification and88.9% F1 for concept extraction) and Spanish (91.4% F1 for concept extraction).

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