Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records (P19-1)
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| Challenge: | De-identification is the task of detecting protected health information (PHI) in medical text. |
| Approach: | They propose to create shareable representations of medical text that contain no PHI and can be shared between organizations to create unified datasets for training de-identification models. |
| Outcome: | The proposed representation allows training a simple LSTM-CRF model to an F1 score of 97.4%. |
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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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| Challenge: | Recent studies show that de-identification is effective in the clinical domain but not in the downstream tasks. |
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