Skip to content

Tutorial for applying machine learning to text data within healthcare

License

Notifications You must be signed in to change notification settings

sempwn/medical-text-nlp

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

medical-text-nlp

Binder Open All Collab

Tutorial for applying machine learning to text data within healthcare

Getting started

These instructions will get you a copy of the notebook up and running on your local machine.

Prerequisites

The easiest way to get up and running is to install Anaconda, which provides python, numerous scientific libraries as well as jupyter. Once Anaconda is installed, navigate to directory for this repository and run jupyter via the command line (using the example directory /Projects/medical-text-nlp/):

cd  /Projects/medical-text-nlp/
jupyter notebook

You can also launch jupyter through the Anaconda Navigator.

Installing

The repository requires the following libraries to be installed via the command-line or within a conda terminal

conda install scikit-learn
conda install pandas
conda install -c anaconda nltk
conda install -c conda-forge wordcloud

Opening notebook

Once the jupyter session is running navigate to the notebook tutorial.ipynb

(Optional) setting up using docker

For enhanced replicability a Docker environment is also provided with the repo, along with a requirements.txt file specifying the Python libraries required. In order to set up the project within this framework run through the following steps

Install Docker Desktop

Build the container by running the following command docker build --pull --rm -f "Dockerfile.dockerfile" -t medicaltextnlp:latest "."

Run docker docker run --rm -d -p 8888:8888/tcp medicaltextnlp:latest

Navigate to the following in a web browser http://localhost:8888/

You will need an access token which can be found in the terminal logs. For more information read the Jupyter Docker Stacks docs.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

About

Tutorial for applying machine learning to text data within healthcare

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published