Sepsis Prediction Pipeline

Project Overview

This project implements an advanced machine learning pipeline for early sepsis detection in healthcare settings. Sepsis is a life-threatening condition that requires rapid detection and treatment, making predictive models extremely valuable for clinical decision support.

Technical Approach

The pipeline follows a comprehensive methodology for detecting sepsis using patient-level clinical data:

Data Preprocessing

• Missing Values: Implemented MICE (Multiple Imputation by Chained Equations) algorithm for sophisticated handling of missing values in temporal medical data
• Feature Engineering: Created 42 clinically relevant features from raw patient measurements, including temporal trends and statistical derivatives
• Data Balancing: Applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance while preserving data integrity
• Scaling: Implemented robust standardization techniques to ensure model stability across heterogeneous feature ranges

Model Development

Implemented and optimized three distinct machine learning models:

1. XGBoost:

   • Achieved exceptional performance with AUROC of 0.9998
   • Optimized hyperparameters using Optuna with 10-fold cross-validation
   • Fine-tuned learning rate, tree depth, and regularization parameters

2. Random Forest:

   • Achieved strong performance with AUROC of 0.9760
   • Tuned for both precision and recall to minimize false positives in clinical setting
   • Optimized tree depth, minimum samples per leaf, and feature subset ratios

3. Logistic Regression:

   • Deployed as baseline comparison model with AUROC of 0.8955
   • Optimized L1/L2 regularization mix for feature selection
   • Implemented probability calibration for improved threshold selection

Evaluation Framework

• Cross-Validation: Implemented stratified 10-fold cross-validation to ensure robust performance estimates
• Metrics: Comprehensive evaluation using AUROC, AUPRC, sensitivity, specificity, and F1-score
• Temporal Validation: Tested model stability across different time periods to ensure consistency
• Visualization: Developed interactive dashboards for model comparison and result interpretation

Clinical Impact

The pipeline demonstrates significant potential for clinical applications:

• Early Detection: Models can identify sepsis up to 6 hours before traditional clinical detection
• Explainability: Feature importance analysis provides clinicians with actionable insights
• Deployment Flexibility: Pipeline designed for both real-time and batch prediction scenarios
• Resource Optimization: Helps prioritize resources for high-risk patients

Future Directions

• Integration with electronic health record systems for real-time alerts
• Expansion to include more diverse patient populations
• Development of customized risk thresholds for different clinical settings
• Implementation of deep learning approaches for even earlier detection capabilities