MedSynthRL is a groundbreaking project that leverages Generative Adversarial Networks (GANs) to generate realistic synthetic patient data. This synthetic data is instrumental in training Reinforcement Learning (RL) agents for medical decision-making and diagnosis within healthcare simulations.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
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GAN-powered Data Generation: Utilize advanced GAN techniques to create synthetic patient data that closely mimics real-world medical scenarios.
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Reinforcement Learning Integration: Train RL agents using the generated synthetic patient data to enhance medical decision-making and diagnosis capabilities.
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Privacy-Preserving Simulation: Address data privacy concerns by using synthetic data, enabling researchers and developers to simulate healthcare scenarios without compromising sensitive patient information.
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Customizable Parameters: Easily adjust GAN and RL parameters to tailor the simulation environment to specific medical use cases.
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Clone the Repository:
git clone https://github.com/smn06/MedSynthRL.git cd MedSynthRL -
Install Dependencies:
pip install -r requirements.txt
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Run the Simulation:
python main_simulation.py
- Adjust GAN parameters in
gan_config.yaml. - Fine-tune RL agent settings in
rl_config.yaml. - Customize medical scenarios and data characteristics in
simulation_config.yaml.
MedSynthRL is licensed under the MIT License - see the LICENSE file for details.