sepsis_prediction

The early prediction of sepsis is potentially life-saving, and we challenge participants to predict sepsis 6 hours before the clinical prediction of sepsis.

Sepsis

Deep learning model for sepsis prediction using high-frequency data

Abstract

Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes

Sepsis occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body. This can cause a cascade of changes that damage multiple organ systems, leading them to fail, sometimes even resulting in death.

Method

1. Data

In this project, we used eICU Collaborative Research Database. The eICU Collaborative Research Database is a large multi-center critical care database made available by Philips Healthcare in partnership with the MIT Laboratory for Computational Physiology.

2. Data PreProcessing

3. Features Selection

4. Model

4. 1.Temporal Convolutional Neural Network

5. Experiment and Results

Conclusion

References