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Table 5 CRF 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
Berg and Dalianis, 2019 [71] 91.00 94.66 86.72
Bui et al. 2017 [72] 93.66 96.29 91.18
Bui et al. 2018 [73] 95.10 98.50 92.00
Du et al. 2018 [74] 98.78 99.27 98.29
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. 2017 [75] 90.74 93.39 88.30
Lee et al. 2017 [76] 90.40 93.46 87.53
Liu et al. 2017 [43] 96.98 97.94 96.04
Phuong et al. 2016 [77] 96.00 97.91 94.16
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