Towards real-time diagnosis for pediatric sepsis using graph neural network and ensemble methods

Area of Science:

• Pediatric critical care medicine
• Artificial intelligence in healthcare
• Sepsis diagnostics

Background:

• Pediatric sepsis presents a rapid threat in ICUs, necessitating swift resuscitation.
• Current sepsis prediction lacks research for short time intervals, delaying critical treatment.

Purpose of the Study:

• To develop a predictive model for real-time sepsis diagnosis in pediatric intensive care units (ICUs).
• To reduce the time to the first antibiotic treatment for pediatric sepsis patients.

Main Methods:

• Utilized data from Shanghai Children's Medical Center, including medical history, physical exams, and six lab test groups.
• Employed a graph neural network for real-time feature extraction and a deep forest model for comprehensive prediction across three data stages.
• Integrated discriminative features from previous stages to optimize global judgment.

Main Results:

• Achieved high Area Under the Curve (AUC) scores: 93.63% (stage 1), 96.73% (stage 2), and 97.58% (full data).
• Obtained strong F1-scores: 77.35% (stage 1), 85.71% (stage 2), and 86.48% (full data).
• Demonstrated accurate predictions at each stage, with stage 2 accuracy approaching full data results.

Conclusions:

• The real-time sepsis prediction model offers superior accuracy compared to control methods.
• Early data input (first two stages) provides near-complete data accuracy, compressing diagnosis time to approximately one hour.
• The model facilitates reduced waiting times and aids in proactive medical resource allocation for sepsis management.