From: A study of deep learning methods for de-identification of clinical notes in cross-institute settings
Model | Embedding | Performance on validation set (i2b2/UTHealth) | |||||
---|---|---|---|---|---|---|---|
Strict | Relax | ||||||
Precision | Recall | F1 score | Precision | Recall | F1 score | ||
LSTM-CRFs | GoogleNews | 0.9679 | 0.9263 | 0.9466 | 0.9783 | 0.9362 | 0.9567 |
CommonCrawl | 0.9697 | 0.9401 | 0.9547 | 0.9797 | 0.9498 | 0.9646 | |
MIMIC-word2vec | 0.9669 | 0.9341 | 0.9502 | 0.9774 | 0.9443 | 0.9606 | |
MIMIC-fastText | 0.9631 | 0.9380 | 0.9504 | 0.9758 | 0.9504 | 0.9629 | |
MADE | 0.9662 | 0.9158 | 0.9403 | 0.9782 | 0.9271 | 0.9520 |