Papers by Anastasios Lamproudis
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. |
Evaluating Pretraining Strategies for Clinical BERT Models (2022.lrec-1)
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| Challenge: | Existing generic language models in specialized domains may be sub-optimal due to domain differences. |
| Approach: | They propose various strategies for adapting a generic language model to the target domain and various forms of vocabulary modifications to fine-tune it. |
| Outcome: | The proposed strategies outperform a general-domain language model but little difference in performance between the models. |