Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (2020.acl-main)
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| Challenge: | Recent studies show that de-identification is effective in the clinical domain but not in the downstream tasks. |
| Approach: | They propose a stacked model with restricted access to privacy sensitive information and a multitask model to investigate the effect of de-identification on clinical concept extraction. |
| Outcome: | The proposed model is stacked with restricted access to privacy sensitive information and a multitask model. |
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| Challenge: | De-identification is the task of detecting protected health information (PHI) in medical text. |
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| Challenge: | De-identification is a natural language processing task to detect and remove the protected health information (PHI) from electronic medical records (EMRs). |
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