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Table 3 Comparison between the state-of-the-art methods and our framework

From: Leveraging text skeleton for de-identification of electronic medical records

Model 2006 i2b2 2014 i2b2 Chinese
Precision Recall F1-score Precision Recall F1-score Precision Recall F1-score
Wellner 0.9870 0.9750 0.9810
Nottingham 0.9900 0.9640 0.9768
MIST 0.9529 0.7569 0.84367
CRF 0.9640 0.9371 0.9504 0.9842 0.9663 0.9752 0.9863 0.9705 0.9783
CRF + ANN 0.9792 0.9784 0.9788
Bi-LSTM 0.9723 0.9656 0.9689 0.9878 0.9389 0.9627 0.9908 0.9584 0.9743
Bi-GRU 0.9871 0.9664 0.9766 0.9750 0.9704 0.9727 0.9898 0.9624 0.9759
TS-GRU 0.9903 0.9855 0.9879 0.9889 0.9723 0.9805 0.9875 0.9719 0.9797