Papers by Thomas Vakili
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022.lrec-1)
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| Challenge: | Automatic de-identification systems introduce errors due to their imperfect precision and may negatively impact the utility of the de-identified dataset. |
| Approach: | They propose to de-identifie a large clinical corpus in Swedish by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. |
| Outcome: | The proposed models are safe to distribute to other academic researchers and reduce privacy risks. |
Data-Constrained Synthesis of Training Data for De-Identification (2025.acl-long)
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| Challenge: | sensitive domains lack widely available datasets due to privacy risks . recent studies have focused on evaluating the privacy of the synthetic text . |
| Approach: | They domain-adapt LLMs to clinical domain and generate synthetic clinical texts . they then generate NER models that can be annotated with tags for PII . |
| Outcome: | The proposed model performs better than the original model using smaller datasets. |