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---
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 breathleness 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]() |
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:
<em>
<b>Developing a BioBERT-based Natural Language Processing Algorithm for Acute Myeloid Leukemia Symptoms Identification from Clinical Notes - Sena Chae , Pratik Maitra , Padmini Srinivasan</b>
</em>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.96 |
| `ENTS_P` | 99.97 |
| `ENTS_R` | 99.94 |
| `TRANSFORMER_LOSS` | 20456.83 |
| `NER_LOSS` | 38920.06 | |