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 | |||
|---|---|---|---|---|
| TRAIN SET - 790 NOTES | TEST SET - 514 NOTES | |||
| PHI LABEL | COUNT | PERCENTAGE | COUNT | PERCENTAGE |
| DATE | 7502 | 43.69 | 4980 | 44.14 |
| STAFF | 3149 | 18.34 | 2004 | 17.76 |
| HOSP | 1437 | 8.37 | 875 | 7.76 |
| AGE | 1233 | 7.18 | 764 | 6.77 |
| LOC | 1206 | 7.02 | 856 | 7.59 |
| PATIENT | 1316 | 7.66 | 879 | 7.79 |
| PHONE | 317 | 1.85 | 217 | 1.92 |
| ID | 881 | 5.13 | 625 | 5.54 |
| PATORG | 124 | 0.72 | 82 | 0.73 |
| 4 | 0.02 | 1 | 0.01 | |
| OTHERPHI | 2 | 0.01 | 0 | 0 |
| TOTAL | 17171 | 100 | 11283 | 100 |
Training procedure
-
Steps on how this model was trained can be found here: Training. The "model_name_or_path" was set to: "roberta-large".
- 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 (16 with 2 gradient accumulation steps)
- Optimizer: AdamW
- Learning rate: 5e-5
- Dropout: 0.1
Results
Questions?
Post a Github issue on the repo: Robust DeID.