README.md

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 Srinivasan

Accuracy

Type	Score
ENTS_F	99.96
ENTS_P	99.97
ENTS_R	99.94
TRANSFORMER_LOSS	20456.83
NER_LOSS	38920.06