Bio-Epidemiology-NER is an Python library built on top of biomedical-ner-all model to recognize bio-medical entities from a corpus or a medical report

| Feature | Output | |---|---| | Named Entity Recognition | Recognize 84 bio-medical entities | | PDF Input | Read Pdf and tabulate the entities| | PDF Annotation | Annotate Entities in a medical pdf report|

Tutorial

Installation

Use the package manager pip to install Bio-Epidemiology-NER

bash pip install Bio-Epidemiology-NER

This package has dependency over Pytorch, please install the required configuration from this link https://pytorch.org/get-started/locally/

Usage

NER with Bio-Epidemiology-NER

```python

load all the functions

from BioEpidemiologyNER.biorecognizer import nerprediction

returns the predicted class along with the probability of the actual EnvBert model

doc = """ CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations. The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea. Except for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings. """

returns a dataframe output

ner_prediction(corpus=doc, compute='cpu') #pass compute='gpu' if using gpu

```

Annotate the entities in a Medical Report and export as pdf/csv format

```python

load all the functions

from BioEpidemiologyNER.biorecognizer import pdfannotate

enter pdf file name

pdffile = 'Alhashash-2020-Emergency surgical management.pdf'

returns a annotated pdf file

pdfannotate(pdffile,compute='cpu', outputformat='pdf') #pass compute='gpu' if using gpu

returns a csv file with entities

pdfannotate(pdffile,compute='cpu', outputformat='csv') #pass compute='gpu' if using gpu

return both annotated pdf and csv file

pdfannotate(pdffile,compute='cpu', outputformat='all') #pass compute='gpu' if using gpu

```

About the Model

The model within this package is an English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (84 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased

• Dataset : Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
• Carbon emission : 0.0279399890043426 Kg
• Training time : 30.16527 minute
• GPU used : 1 x GeForce RTX 3060 Laptop GPU

for more details regarding the entities supported, check the config file https://huggingface.co/d4data/biomedical-ner-all/blob/main/config.json

Ownership & License

This Package is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset),

Please cite our Research Paper

and star at: https://github.com/dreji18/biomedicalNER

MIT License

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