This repository houses a machine learning project focused on the early detection and classification of sepsis, and integrating the model into a web application using FAST API.
This project aims to provide a streamlined tool for healthcare professionals to predict sepsis cases quickly and effectively.
| Jupyter Notebook | Published Article | Link To Working App on Hugging Face |
|---|---|---|
| Notebook with code and full analysis | Published Article on Medium | Link to working FastAPI |
i. Data Collection and Preprocessing: We collected and preprocessed a comprehensive dataset containing clinical and physiological data from patients to train and evaluate our sepsis classification model.
ii. Machine Learning Model: We implemented a state-of-the-art machine learning model tailored for sepsis classification. This model has been fine-tuned to achieve high accuracy in detecting sepsis early, which is crucial for timely intervention.
iii. FAST API Integration: We've seamlessly integrated the trained machine learning model into a web application using FAST API. This web application allows healthcare professionals to input patient data and receive instant predictions regarding sepsis risk.
iv. Usage and Deployment: In the README file, you will find detailed instructions on how to use and deploy this web application, making it user-friendly for both developers and healthcare practitioners.
- Machine Learning Model (Code and Weights)
- FAST API Web Application Code
- Data Preprocessing Scripts
- Documentation
- Example Usage
- Dependencies and Installation Instructions
- Clone this repository to your local machine.
- Install the required dependencies.
- Run the FAST API application.
- Access the web application through a web browser.
- Input patient data and receive sepsis risk predictions.
Your contributions are welcome to improve the model's performance, add new features, or enhance the web application's usability. Please refer to our contribution guidelines in the repository to get started.
This project is licensed under the MIT License.
We would like to thank the open-source community and the healthcare professionals who contributed to the dataset used in this project. Their efforts have made advancements in sepsis detection possible.
Feel free to explore the code, use the web application, and contribute to the project's development. Early sepsis detection can save lives, and together, we can make a difference in healthcare.