Research Article
Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts
Table 6
Comparison of symptom extraction performance for different models on the WithNeg dataset.
| Models | Precision | Recall | F1-score | Normal | Degraded | Normal | Degraded | Normal | Degraded |
| BiLSTM-CRF | 0.9134 | 0.6111 | 0.9090 | 0.9133 | 0.9112 | 0.7322 | GloVeWiki-BiLSTM-CRF | 0.9077 | 0.6076 | 0.9085 | 0.9124 | 0.9081 | 0.7294 | GloVeMedical-BiLSTM-CRF | 0.9119 | 0.6120 | 0.9077 | 0.9127 | 0.9098 | 0.7327 | W2VWiki-BiLSTM-CRF | 0.9363 | 0.6133 | 0.9213 | 0.9287 | 0.9287 | 0.7387 | W2VMedical-BiLSTM-CRF | 0.9329 | 0.6144 | 0.9281 | 0.9321 | 0.9305 | 0.7406 | BERT-CRF | 0.9261 | 0.6211 | 0.9243 | 0.9254 | 0.9252 | 0.7433 | Phenonizer | 0.9405 | 0.6216 | 0.9387 | 0.9398 | 0.9396 | 0.7483 |
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