GANFHIR
GANFHIR is a Python repository that utilizes Generative Adversarial Networks (GANs) to generate synthetic healthcare data in the Fast Healthcare Interoperability Resources (FHIR) format. This README file provides an overview of the repository and instructions on how to run the GAN and FHIR scripts.
Dependencies
Torch: A popular deep learning framework
TorchText: A library for natural language processing (NLP) tasks in PyTorch
Installation Make sure you have Python installed (Python 3.6 or higher).
Install the required dependencies
pip install torch torchtext
Usage
Training the Model (GAN.py)
To train the GAN model, follow these steps:
- Clone the GANFHIR repository:
git clone https://github.com/your-username/GANFHIR.git
- Change into the GANFHIR directory:
cd GANFHIR
#Run the GAN.py script:
python GAN.py
This will initiate the GAN training process. The script will load the necessary datasets, define and train the GAN model, and save the trained model parameters.
Note: Adjust the hyperparameters and training configurations in the GAN.py script according to your needs.
Generating FHIR Data (FHIR.py)
Once you have trained the GAN model, you can generate synthetic FHIR data by following these steps:
python FHIR.py
The script will load the trained GAN model parameters, generate synthetic data, and save the generated FHIR resources.
Note: Make sure to update the FHIR.py script with the appropriate paths to the trained model parameters and output directory.
Dataset The GANFHIR repository does not include a dataset by default. It is expected that you provide your own dataset in the appropriate format for GAN training. Please ensure that the dataset is in a compatible format before running the GAN.py script.
Contributing Contributions to the GANFHIR repository are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request. We appreciate your contributions to make this project better.
License This repository is licensed under the MIT License. Feel free to use, modify, and distribute the code for your purposes.
Acknowledgments We would like to thank the contributors and the open-source community for their valuable contributions to the tools and libraries used in this project. Your efforts are greatly appreciated.
Contact If you have any questions or inquiries, please contact us at [email protected]. We would be happy to assist you.
Happy GANFHIRing!