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Related Experiment Video

Updated: Oct 18, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
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A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

346

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept

Inger Persson1,2, Andreas Östling1, Martin Arlbrandt3

  • 1Department of Statistics, Uppsala University, Uppsala, Sweden.

JMIR Formative Research
|September 30, 2021
PubMed
Summary

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This summary is machine-generated.

A new machine learning algorithm, NAVOY Sepsis, can predict sepsis onset up to three hours in advance using routine intensive care unit data. This offers high performance for early detection and improved patient outcomes.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Critical Care Medicine

Background:

  • Sepsis is a major cause of death in intensive care units (ICUs) globally.
  • Early detection is crucial for effective sepsis management and improved patient outcomes.
  • Currently, no prospectively validated machine learning (ML) prediction algorithm for sepsis is clinically available in Europe.

Purpose of the Study:

  • To develop a high-performance ML algorithm for sepsis prediction.
  • To design the algorithm for implementation in European ICUs using routinely collected data.

Main Methods:

  • Convolutional neural networks (CNNs) were used to develop the ML algorithm.
  • The model was trained on MIMIC-III clinical data from adult ICU patients.
Keywords:
EHRICUalgorithmdetectionearly detectionelectronic health recordintensive care unitmachine learningpredictionproof of conceptsepsissoftware as a medical device

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  • Hourly predictions of sepsis onset (Sepsis-3 criteria) were generated using 20 variables.

    • Main Results:

    • The NAVOY Sepsis algorithm achieved an area under the receiver operating characteristics curve of 0.90.
    • It demonstrated an area under the precision-recall curve of 0.62.
    • The algorithm can predict sepsis onset up to 3 hours in advance with high accuracy.

    Conclusions:

    • NAVOY Sepsis shows superior prediction performance compared to existing early warning scoring systems.
    • Its performance is comparable to other sepsis prediction algorithms.
    • The algorithm exhibits excellent predictive properties and utilizes routinely collected ICU variables.