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Related Experiment Video

Updated: Jan 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

463

Improving Machine Learning Models of Late-onset Sepsis Prediction Using Optimized Control Group Selection: A

Zheng Peng, Hendrik Niemarkt, Xi Long

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

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    Choosing the right control group is crucial for accurately predicting late-onset sepsis (LOS) in preterm infants. A combined strategy significantly improves prediction accuracy, enhancing clinical management in neonatal intensive care units (NICUs).

    Area of Science:

    • Neonatal Medicine
    • Computational Biology
    • Clinical Informatics

    Background:

    • Late-onset sepsis (LOS) is a critical threat to preterm infants in neonatal intensive care units (NICUs), driven by immature immunity and complex environments.
    • Predictive models using vital signs show promise for early LOS detection, but inconsistent control group selection hinders performance.
    • Systematic comparison of control group strategies is needed to optimize LOS prediction models.

    Purpose of the Study:

    • To evaluate the impact of three distinct control group selection strategies on the performance of late-onset sepsis (LOS) prediction models.
    • To compare prediction accuracy using independent control, intra-LOS control, and a combined strategy.
    • To identify the optimal control group strategy for enhancing LOS detection in preterm infants.

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    A Data-Driven Approach to Quantifying Immune States in Sepsis
    07:42

    A Data-Driven Approach to Quantifying Immune States in Sepsis

    Published on: February 7, 2025

    463

    Main Methods:

    • Trained and evaluated three LOS prediction models using different control group strategies: independent, intra-LOS, and combined.
    • Utilized a training dataset of 128 preterm infants (60 LOS, 68 controls) and an independent test set of 49 patients.
    • Assessed model performance using Area Under the Curve (AUC), sensitivity, specificity, PPV, and NPV across various prediction windows.

    Main Results:

    • The combined strategy achieved the highest AUC (86.2%) within a 3-hour prediction window, surpassing independent (70.9%) and intra-LOS (78.2%) strategies.
    • Independent strategy yielded higher sensitivity and NPV, while intra-LOS offered better specificity and PPV.
    • The combined strategy balanced these metrics, demonstrating superior overall predictive performance.

    Conclusions:

    • Control group selection significantly impacts the performance and generalizability of LOS prediction models.
    • The combined control group strategy presents a promising approach for improving the accuracy of early LOS detection in preterm infants.
    • Optimized predictive models can enhance clinical decision-making and management of LOS in neonatal care.