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Table 7 Comparison of model performance metrics

From: Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT)

Embedding

Classifier

Accuracy

Precision

Recall

AUC

F1-score

Word2Vec

CNN

0.729

0.744

0.729

0.767

0.733

 

MLP

0.644

0.643

0.644

0.711

0.644

 

Bi-LSTM

0.737

0.740

0.737

0.677

0.738

 

Bi-LSTM-CNN

0.728

0.729

0.728

0.692

0.728

BERT

CNN

0.770

0.788

0.777

0.908

0.781

 

MLP

0.719

0.714

0.719

0.874

0.712

 

Bi-LSTM

0.777

0.792

0.780

0.888

0.774

 

Bi-LSTM-CNN

0.698

0.696

0.698

0.861

0.690

 

Fine-tune

0.760

0.761

0.759

0.868

0.760

 

IDPT

0.842

0.843

0.842

0.948

0.841

BERT-wmm-ext

Fine-tune

0.756

0.756

0.756

0.883

0.754

Mengzi

Fine-tune

0.751

0.751

0.751

0.846

0.750

Roberta

Fine-tune

0.767

0.767

0.767

0.878

0.764

  1. The highest index is highlighted in bold