Daniel Steinbach, Paul C Ahrens, Maria Schmidt, Martin Federbusch, Lara Heuft, Christoph Lübbert, Matthias Nauck, Matthias Gründling, Berend Isermann, Sebastian Gibb, Thorsten Kaiser
BACKGROUND: Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. METHODS: We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission...
March 2, 2024: Clinical Chemistry