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Found 9 papers, 1 papers with code
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data
The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks.
Evaluating Pretraining Strategies for Clinical BERT Models
Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model.
The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text
The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text.
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data
The use of pretrained language models, fine-tuned to perform a specific downstream task, has become widespread in NLP.
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Contaminated or adulterated food poses a substantial risk to human health.
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