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- A ClinicalBERT [Alsentzer 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.
- 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] dataset was used to train this model.
|TRAIN SET - 790 NOTES||TEST SET - 514 NOTES|
- Steps on how this model was trained can be found here: Training. The "model_name_or_path" was set to: "emilyalsentzer/Bio_ClinicalBERT".
- The dataset was sentencized with the en_core_sci_sm sentencizer from spacy.
- The dataset was then tokenized with a custom tokenizer built on top of the en_core_sci_sm tokenizer from spacy.
- For each sentence we added 32 tokens on the left (from previous sentences) and 32 tokens on the right (from the next sentences).
- The added tokens are not used for learning - i.e, the loss is not computed on these tokens - they are used as additional context.
- Each sequence contained a maximum of 128 tokens (including the 32 tokens added on). Longer sequences were split.
- The sentencized and tokenized dataset with the token level labels based on the BILOU notation was used to train the model.
- The model is fine-tuned from a pre-trained RoBERTa model.
- Training details:
- Input sequence length: 128
- Batch size: 32
- Optimizer: AdamW
- Learning rate: 4e-5
- Dropout: 0.1
Post a Github issue on the repo: Robust DeID.
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This model can be loaded on the Inference API on-demand.