Zenodo
Sepsis Early Warning: External Validation of a Physiology-Informed Machine Learning Model Across Hospital Systems
By Ajibola Odeyemi.
Abstract
Background
Sepsis remains a leading cause of in-hospital mortality worldwide. Delayed recognition significantly worsens clinical outcomes, and existing scoring tools are typically applied only after clinical suspicion has already been raised.
Methods
This study developed a physiology-informed LightGBM machine learning model to predict sepsis onset up to 6 hours in advance, using ICU data from two independent hospital systems (PhysioNet/Computing in Cardiology Challenge 2019). Set A (n = 20,336) was used for model development and internal testing; Set B (n = 20,000), from a different hospital system, was reserved exclusively for external validation without retuning. Features were engineered causally, using only information available up to each hour, and the alert threshold was selected strictly on validation data before any evaluation on Set B. Confidence intervals were computed using patient-level bootstrap resampling (100 iterations).
