Research Article
Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts
Table 8
The symptom extraction performance of models on isomorphic data (COVID-19).
| Training dataset | COVID-19 | TCM-HN | Models | Precision | Recall | F1-score | Precision | Recall | F1-score |
| BiLSTM-CRF | 0.9128 | 0.9127 | 0.9128 | 0.7739 | 0.7675 | 0.7707 | GloVeWiki-BiLSTM-CRF | 0.9064 | 0.9116 | 0.9090 | 0.7626 | 0.7715 | 0.7670 | GloVeMedical-BiLSTM-CRF | 0.9093 | 0.9144 | 0.9113 | 0.7683 | 0.7661 | 0.7672 | W2VWiki-BiLSTM-CRF | 0.9145 | 0.9209 | 0.9177 | 0.7994 | 0.8380 | 0.8181 | W2VMedical-BiLSTM-CRF | 0.9164 | 0.9201 | 0.9183 | 0.8104 | 0.8457 | 0.8275 | BERT-CRF | 0.9220 | 0.9231 | 0.9225 | 0.8056 | 0.8440 | 0.8243 | BERT-BiLSTM | 0.9170 | 0.9220 | 0.9195 | 0.8188 | 0.8516 | 0.8348 | Phenonizer | 0.9211 | 0.9264 | 0.9237 | 0.8230 | 0.8556 | 0.8389 |
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