deid_roberta_i2b2 EU AI Act Compliance Profile

Model Description

• A RoBERTa [Liu et al., 2019] model fine-tuned for de-identification of medical notes.
• Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information categories is given by HIPAA.
• A token can either be classified as non-PHI or as one of the 11 PHI types. Token predictions are aggregated to spans by making use of BILOU tagging.
• The PHI labels that were used for training and other details can be found here: Annotation Guidelines
• More details on how to use this model, the format of data and other useful information is present in the GitHub repo: Robust DeID.

How to use

• A demo on how the model works (using model predictions to de-identify a medical note) is on this space: Medical-Note-Deidentification.
• Steps on how this model can be used to run a forward pass can be found here: Forward Pass
• In brief, the steps are:
  • Sentencize (the model aggregates the sentences back to the note level) and tokenize the dataset.
  • Use the predict function of this model to gather the predictions (i.e., predictions for each token).
  • Additionally, the model predictions can be used to remove PHI from the original note/text.

Dataset

• The I2B2 2014 [Stubbs and Uzuner, 2015] dataset was used to train this model.

| | I2B2 | | I2B2 | | | --------- | --------------------- | ---------

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