Papers with de-identification models
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text (D19-62)
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| Challenge: | Several ethical and scientific issues arise regarding the balance between maintaining patient confidentiality and the need for wider application of trained models. |
| Approach: | They propose to use pseudonymised clinical text as training data to de-identify real clinical text in other hospitals. |
| Outcome: | The proposed model performed better for some PHI information than the standard model and poor performance on Location and Health Care Unit information. |
Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches (2026.findings-eacl)
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| Challenge: | a recent study has not examined their generalizability between formats, cultures, and genders. |
| Approach: | They evaluate large language models (LLMs) and small LLMs at clinical de-identification . they show that smaller models achieve comparable performance while substantially reducing inference cost . |
| Outcome: | The proposed models outperform larger models in de-identification tasks with limited data . the models can be fine-tuned with limited datasets to outperformed larger models . |
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%. |