An interpretable machine learning algorithm enables dynamic 48-hour mortality prediction during an ICU stay.

ABSTRACT:

Backround

Accurate short-term mortality prediction is essential for optimizing ICU management and improving patient outcomes. Many existing models rely on static data and do not reflect the dynamic progression of critical illness. This study aimed to develop and validate an interpretable machine learning algorithm that enables dynamic 48-hour mortality prediction throughout the ICU stay.

Methods

We conducted a retrospective cohort study using electronic health records of 9,786 ICU patients treated between 2018 and 2022 at a German university hospital. A machine learning model was developed to predict 48-hour mortality, updated every 24 hours during the ICU stay. We trained and evaluated a Light Gradient-Boosting Machine using nested cross-validation and assessed performance

SUBMITTER: Britsch S 

PROVIDER: S-EPMC12528449 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature