Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining

Table 4

Comparisons of different recognition models and different word embedding.

Models	Dataset	Marco-P (%)	Marco-R (%)	Marco-F1 (%)
BiLSTM-CRF + embedding	Second	68.37	70.84	69.58
BiLSTM-CRF + EMR embedding	First	72.48	72.54	72.51
Transformer-CRF + embedding	Second	52.70	69.50	59.90
Transformer-CRF + EMR embedding	First	52.70	72.10	60.70