Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients

• DOI: 10.1093/bioinformatics/btac229
• Publication Date: 2022-04-14T11:10:15Z
• Authors: Bowen Fan; Juliane Klatt; Michael M Moor; Latasha A Daniels; ; Philipp K A Agyeman; Christoph Berger; Eric Giannoni; Martin Stocker; Klara M Posfay-Barbe; Ulrich Heininger; Sara Bernhard-Sti...; Anita Niederer-Loher; Christian R Kahlert; Giancarlo Natalucci; Christa Relly; Thomas Riedel; Christoph Aebi; Luregn J Schlapbach; Lazaro N Sanchez-...; Philipp K A Agyeman; Luregn J Schlapbach; Karsten M Borgwardt
• Keywords: Organ dysfunction; Electronic health record; Triage; Bacteremia

Sepsis is a leading cause of death and disability in children globally, accounting for ∼3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) considered significant risk factor adverse clinical outcomes characterized by high mortality morbidity intensive care unit. The recent rapidly growing availability electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning healthcare achieved great successes. However, effective models which could make accurate early prediction recovery patients from MODS mild state thus assist clinicians decision-making process still lacking.