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--- |
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license: mit |
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pipeline_tag: token-classification |
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tags: |
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- BERT |
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- bioBERT |
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- NER |
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- medical |
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metrics: |
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- f1 |
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language: |
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- en |
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--- |
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# Model |
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NER-Model for disease/treatment entity recognition. The purpose of the model/data use is educational. |
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The original dataset tags have been augmented with "inside"-Tags in order to handle sub-tokens produced by the WordPiece tokenizer. Following NER-tags are used: |
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* `B-D`, `I-D`: begin and inside tags for disease |
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* `B-T`, `I-T`: begin and inside tags for treatment |
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* `O` - outside entities (irrelevant) |
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``` |
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# Text: |
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Acute obstructive hydrocephalus complicating bacterial meningitis in childhood |
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# Real: |
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Acute -> D |
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obstructive -> D |
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hydrocephalus -> D |
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bacterial -> D |
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meningitis -> D |
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# Predictions: |
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o##bs##truct##ive -> B-D + I-D + I-D + I-D |
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h##ydro##ce##pha##lus -> B-D + I-D + I-D + I-D + I-D |
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bacterial -> B-D |
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men##ing##itis -> B-D + I-D + I-D |
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``` |
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# Sources |
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This pipeline is based on the [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) pretrained model, |
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fine-tuned using the relatively small [BeHealthy Medical Entity](https://www.kaggle.com/datasets/arunagirirajan/medical-entity-recognition-ner) |
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dataset (1.550 training samples). |
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# Performance |
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The model has not been extensively tuned. The quality of the dataset is not clear, due to unknown origin of the data / annotation process. |
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|Metric |Score | |
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|---------|----------| |
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Precision | 0.854523 | |
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Recall | 0.859779 | |
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F1 | 0.857143 | |
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Accuracy | 0.919590 | |
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