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deid_bert_i2b2

obi

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* A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) 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. * 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. * The I2B2 2014 [[Stubbs and Uzuner, 2015]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978170/) dataset was used to train this model.

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deid_bert_i2b2

deid_roberta_i2b2

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