A secure and ethical way to clinical-text analysis & extraction
Website | Contributing | Features
With the emergence of new techniques of machine learning, and the possibility of using algorithms to perform tasks previously done by human beings, as well as to generate new knowledge, we again face a set of new ethical questions. These questions not only concern the possibility of harm by the misuse of data but also questions of how to preserve privacy where data is sensitive, how to avoid bias in data selection, how to prevent disruption and hacking of data, and issues of transparency in data collection, research, and dissemination.
- Core Feature Protection of raw data to maintain privacy and prevent disruption and hacking of the same by anonymizing the sensitive information. On doing so, the data is now private enough to be processed for unstructured information management and analysis.
ctakes-ext is an open source project, and any contributions to the project is highly appreciated and encouraged. Feel free to open issues to report bugs or request new features.
Make sure to Fork this repository into your account before making any commits. Then use the following commands to set up the project.
#Fork & clone the repository to the local directory
git clone https://github.com/<your-github-username>/ctakes-ext
# Run the backend
cd backend
pip install -r requirements-dev.txt
python3 manage.py runserver
#Run using Docker
docker-compose build
docker-compose up
#Swich to branch frontend
cd frontend
npm install
ctakes-ext is a project for Sprint 2 of the MLH Fellowship. Here's a demo video that was made for the submission. This might help you understand the project better. Demo Video