Recognize bio-medical entities from a text corpus
Project description
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 |
Installation
Use the package manager pip to install Bio-Epidemiology-NER
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
# load all the functions
from Bio_Epidemiology_NER.bio_recognizer import ner_prediction
# 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
# load all the functions
from Bio_Epidemiology_NER.bio_recognizer import pdf_annotate
# enter pdf file name
pdffile = 'Alhashash-2020-Emergency surgical management.pdf'
# returns a annotated pdf file
pdf_annotate(pdffile,compute='cpu', output_format='pdf') #pass compute='gpu' if using gpu
# returns a csv file with entities
pdf_annotate(pdffile,compute='cpu', output_format='csv') #pass compute='gpu' if using gpu
# return both annotated pdf and csv file
pdf_annotate(pdffile,compute='cpu', output_format='all') #pass compute='gpu' if using gpu
# if you come across any pdf issues, please install PyMuPDF 1.20.1 version
About
This model 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 us and star at: https://github.com/dreji18/biomedicalNER
License
MIT License
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file Bio_Epidemiology_NER-0.1.3.tar.gz
.
File metadata
- Download URL: Bio_Epidemiology_NER-0.1.3.tar.gz
- Upload date:
- Size: 4.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | fe93c83d12164a029915a1ae7b324730fb902f3046786d4512996bdb3514da7c |
|
MD5 | 8c36ad9a7cb1831a6b5ad2591f458a69 |
|
BLAKE2b-256 | 6b28b4c778a93326748b1380f7ee786e0e43659679506741e9da091601d8d4c8 |
File details
Details for the file Bio_Epidemiology_NER-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: Bio_Epidemiology_NER-0.1.3-py3-none-any.whl
- Upload date:
- Size: 4.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 270e717c861ebb88745b712fc4ccd9a7a047fd665b6a9ea06396c0f672c4c786 |
|
MD5 | 195d8a68622286fb85054cce74f4bcdb |
|
BLAKE2b-256 | 9cd3662d3247376e1422b7678218ee5c59f8c666f43ea3dbec8908aad154d5c7 |