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Table 2 RNN papers on de-identification of medical free text

From: Survey on RNN and CRF models for de-identification of medical free text

Author F-measurea Precisiona Recalla
Dernoncourt et al. 2017 [33] 99.23 99.21 99.25
Jiang et al. 2017 [38] 91.45 93.24 89.72
Kajiyama et al. 2018 [39] 80.61 Not provided Not provided
Kim et al. 2018 [40] 95.73 97.04 94.45
Lee et al. 2016 [41] 99.26 99.21 99.31
Lee et al. 2019 [42] 89.00 90.88 87.2
Liu et al. 2017 [43] 96.98 97.94 96.04
Madan et al. 2018 [44] 95.92 Not provided Not provided
Richter et al. 2019 [45] 96.00 97.00 95.50
Shweta et al. 2016 [46] 93.84 97.26 90.67
Trienes et al. 2020 [47] 91.20 95.90 86.90
Yang et al. 2019 [48] 96.46 97.97 94.98
  1. aAll scores shown are percentages