Multi-omic admission-based prognostic biomarkers identified by machine learning algorithms predict patient recovery and 30-day survival in trauma patients

HIGHLIGHTS

• who: Sultan S. Abdelhamid and collaborators from the Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA have published the research: Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients, in the Journal: Metabolites 25% of 18/08/2022
• what: The authors report here the application of machine_learning algorithms to a database of proteomic, lipidomic, and metabolomic plasma markers to identify admission-based prognostic biomarkers for adverse outcomes. To validate the accuracy of the models, proteomic data from the standard-of-care arm . . .
  

   

  

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