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This Python script retrieves patient data from a FHIR server, processes it into a DataFrame, and trains a basic machine learning model to predict sepsis (using simulated labels). It includes data validation, feature engineering, model evaluation, and SMOTE for class imbalance.

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How to Use:

Install Dependencies:

pip install requests pandas scikit-learn matplotlib seaborn imbalanced-learn

Configure FHIR Server (Optional): Replace 'http://hapi.fhir.org/baseR4/Patient' with the actual URL of your FHIR server. If you want to use the example bundle, no need to change the url. Run the Script:

python your_script_name.py Important Notes:

The sepsis labels are randomly assigned for demonstration purposes. In a real-world scenario, you would need to use actual clinical data to label patients. This script provides a basic example and may need to be modified to fit specific requirements. Judges can test the script with the provided example patient bundle, or by changing the use_example_bundle variable to False, and providing a valid FHIR server URL. The script includes SMOTE to handle class imbalance, which is a common issue in medical datasets.

About

This Python script retrieves patient data from a FHIR server, processes it into a DataFrame, and trains a basic machine learning model to predict sepsis (using simulated labels). It includes data validation, feature engineering, model evaluation, and SMOTE for class imbalance.

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