metadata
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_biobert_ner_symptom
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9997017596
- name: NER Recall
type: recall
value: 0.9994036971
- name: NER F Score
type: f_score
value: 0.9995527061
widget:
- text: Patient X reported coughing and sneezing.
example_title: Example 1
- text: There was a case of rash and inflammation.
example_title: Example 2
- text: He complained of dizziness during the trip.
example_title: Example 3
- text: I felt distressed , giddy and nauseous during my stay in Florida.
example_title: Example 4
BioBERT based NER model for medical symptoms
| Feature | Description |
|---|---|
| Name | en_biobert_ner_symptom |
| Version | 1.0.0 |
| spaCy | >=3.5.1,<3.6.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | MIT |
| Author | Sena Chae, Pratik Maitra, Padmini Srinivasan |
The model was trained on the combined maccrobat and i2c2 dataset and is based on biobert. If you use the model kindly cite the paper below:
Developing a BioBERT-based Natural Language Processing Algorithm for Acute Myeloid Leukemia Symptoms Identification from Clinical Notes - Sena Chae , Pratik Maitra , Padmini SrinivasanAccuracy
| Type | Score |
|---|---|
ENTS_F |
99.96 |
ENTS_P |
99.97 |
ENTS_R |
99.94 |
TRANSFORMER_LOSS |
20456.83 |
NER_LOSS |
38920.06 |