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Table 5 Comparison results of MicroP, MicroR and MicroF measure on progress notes

From: A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records

Model

MicroP

MicroR

MicroF

Naive Bayes

79.42

79.37

79.40

Maximum Entropy

91.45

91.45

91.45

Support Vector Machine

93.07

93.06

93.06

Conditional Random Field [7]

94.93

94.02

94.02

Convolutional Neural Network [7]

91.13

91.14

91.13

Bi-RNN model

93.58

93.58

93.58

Transfer learning Bi-RNN model [24]

94.37

94.37

94.37

Our proposed model

96.65

96.65

96.65