This repository contains the supporting code for the article titled "Prediction of Postoperative Infections by Strategic Data Imputation and Explainable Machine Learning". It includes a pre-trained model, executable code, and a notebook designed for Google Colab to facilitate replication and further exploration of the study's findings.
- /data: Contains the pre-trained machine learning models used in the study.
- /nb_predictor.ipynb: Jupyter notebook for performing the classification and SHAP Radar.
To get started with this project, clone this repository using:
git clone https://github.com/HugoGuillen/postsurgicalinfections.git##Prerequisites
Ensure you have the following installed:
- Python 3.8 or later
- Jupyter Notebook or access to Google Colab
- Necessary Python libraries which can be installed using:
pip install -r requirements.txt
- Navigate to the root directory.
- Open the Jupyter Notebook in your local environment or upload it to Google Colab.
- Follow the instructions within the notebook to run the model.
To use the pre-trained model:
- Import the model from the /model directory.
- Load your data following the structure outlined in the notebook (check the
template.csv). - Use the provided code in the notebook to apply the model to your data.
Contributions to this project are welcome. Please fork the repository and submit a pull request with your proposed changes.
If you use the code or models from this repository in your research, please cite the associated article: IN REVIEW
This project is licensed under the Apache 2.0 License.
For any queries, please reach out via email at hugo.guillenramirez@unibe.ch.