This project aims to predict the need for shunt in subarachnoid hemorrhage (SAH) patients from intensive care unit (ICU) using a Long Short-Term Memory (LSTM) model.
The dataset used for this project was obtained from the University Medical Center Hamburg-Eppendorf
We used an LSTM model to predict the need for shunt in SAH patients. The model takes multiple ICU parameters as input and outputs a binary classification (0 for no shunt needed, 1 for shunt needed). The LSTM model is trained on the training set and validated on the validation set to prevent overfitting in a nested-k-fold regime. The final model is evaluated on the test set to assess its performance.
- Python 3
- Scikit-learn
- Pandas
- NumPy
- Pytorch
Clone this repository:
git clone https://github.com/agschweingruber/sah.git cd sah
Install the required packages:
cd ./training pip install -r requirements.txt
Run the train_Shunt.ipynb script to train the model
We thank the Department of Neurosurgery, Neurology, Neuroradiology and the Intensive Care Unit at the University Medical Center Hamburg-Eppendorf for their support.