README.md · pmaitra/en_biobert_ner

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 breathlessness and chest pain when he was driving back
      to his house.
    example_title: Example 5

Fine-tuned BioBERT based NER model for detecting medical symptoms from clinical notes.

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:

Uncovering Hidden Symptom Clusters in Patients with Acute Myeloid Leukemia using Natural Language Processing - Sena Chae, Jaewon Bae , Pratik Matira, Karen Dunn Lopez, Barbara Rakel

Model Usage

The model can be loaded using spacy after installing the model.

!pip install https://huggingface.co/pmaitra/en_biobert_ner_symptom/resolve/main/en_biobert_ner_symptom-any-py3-none-any.whl

A sample use-case is presented below:


import spacy
nlp = spacy.load("en_biobert_ner_symptom")

doc = nlp("He complained of dizziness and nausea during the Iowa trip.")

for ent in doc.ents:
  print(ent)

Accuracy

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