Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients

Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 m...

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Bibliographic Details
Main Authors: Sultan S. Abdelhamid, Jacob Scioscia, Yoram Vodovotz, Junru Wu, Anna Rosengart, Eunseo Sung, Syed Rahman, Robert Voinchet, Jillian Bonaroti, Shimena Li, Jennifer L. Darby, Upendra K. Kar, Matthew D. Neal, Jason Sperry, Jishnu Das, Timothy R. Billiar
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Metabolites
Subjects:
multi-omics
trauma
machine learning
biomarkers
prognostic
proteomics
Online Access:https://www.mdpi.com/2218-1989/12/9/774

Internet

https://www.mdpi.com/2218-1989/12/9/774

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