Early Prediction of Sepsis From Clinical data (PhysioNet Challenge 2019)

Sepsis is a life-threatening condition that occurs when the body's response to infection causes tissue damage, organ failure, or death. Early detection and antibiotic treatment of sepsis are critical for improving sepsis outcomes, where each hour of delayed treatment has been associated with roughly an 4-8% increase in mortality. The goal of PhysioNet Challenge 2019 is the early detection of sepsis using physiological data.

In this Repository

• Data.md contains the challenge data description and how to access data.
• Cluster contains scripts to prepare needed libraries and implement the proposed system on a cluster.
• Evaluation contains the script to evaluate prediction results using Utility Function that was created by the Challenge.
• Imputation contains the script and requirement data to fill missing values in the challenge data.
• Train_Prediction contains scripts to train the AdaBoost model and predict the test data along with the proposed AdaBoost model.

How to Implement

You have to follow below steps to implement and evaluate the proposed Early Prediction of Sepsis.

• Download data
• Fill missing data
• Train the ptoposed AdaBoost model and predict test samples
• Evaluate results using the utility function