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 Support | feature coming soon... |
Installation
Use the package manager pip to install Bio_Epidemiology_NER
pip install Bio_Epidemiology_NER
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=text, compute='gpu') #pass compute='cpu' if using cpu
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
Close
Hashes for Bio_Epidemiology_NER-0.0.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0eb931d627300fde7775a73558fbe1dcc0901c5161250c47ba9901a3e46f3be9 |
|
MD5 | 9e7d9418b9b1ddfaede538e23f685642 |
|
BLAKE2b-256 | feff842a8584f0b46e61c91189937b75c9f21669f740ef24fb3673f149d5502c |
Close
Hashes for Bio_Epidemiology_NER-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 992bc77f56b5728bde4fd9433c9910450bd329534e74e407a074eb08665acbac |
|
MD5 | 42124e53afac755bee1225ad03e860d3 |
|
BLAKE2b-256 | a0e921caf0992080cc0b984ea3a42a119c896309d834e98d51b2690cfba0ce10 |