Skip to main content

Table 4 Evaluation metrics for each of the different neural networks tested

From: De-identifying Spanish medical texts - named entity recognition applied to radiology reports

  Training set Validation set Test set
Model Precision Recall F1 Precision Recall F1 Precision Recall F1
LSTM-CRF 90.39 81.93 85.95 87.09 77.11 81.79 81.35 61.37 69.96
LSTM-CRF with EMA 91.19 84.15 87.53 87.05 78.49 82.55 71.48 59.65 64.96
LSTM-LSTM-CRF 99.20 98.79 98.99 98.13 97.18 97.66 93.01 90.94 91.96
LSTM-LSTM-CRF with EMA 99.06 98.96 99.01 98.00 97.34 97.67 94.20 91.10 92.63
Conv-LSTM-CRF 99.31 99.05 99.18 98.11 97.29 97.70 94.49 90.43 92.41
Conv-LSTM-CRF with EMA 99.17 99.05 99.11 98.08 97.36 97.72 93.72 90.64 92.15
Spacy 99.87 99.28 99.58 98.06 96.10 97.07 93.23 89.39 91.31
  1. Bold font highlights the best metric in each data subset