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
  - text: >-
      Mr. Y complained of breathlesness and chest pain when he was driving back
      to his house.
    example_title: Example 5

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

Model Description

The model was trained on a combined maccrobat and i2c2 dataset and is based on biobert. If you use this 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

How to use the Model

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")

Accuracy

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