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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