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Paper Details

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.
Front Immunol
36
2021
CLEC5A, Genes, HCAR3, MS4A3, NLRP1, OLAH, PLCB1, Peripheral Blood Immune Cells, SDC4, Sepsis, TCN1, critically ill, differentially expressed genes, gene, multiorgan dysfunction, neutrophil, patient, patients, peripheral blood cells, sepsis, septic
Biomarkers, Chromosome Mapping, Computational Biology, Critical Care, Databases, Genetic, Disease Susceptibility, Gene Expression Profiling, Hospital Mortality, Humans, Intensive Care Units, Leukocytes, Machine Learning, ROC Curve, Reproducibility of Results, Sepsis, Time Factors, Transcriptome
Author NameAffiliation
Shayantan BanerjeeUniversity of Tennessee Health Science Center
Shayantan BanerjeeIndian Institute of Technology Madras
Akram MohammedUniversity of Tennessee Health Science Center
Hector R WongCincinnati Children's Hospital Medical Center
Nades PalaniyarPeter Gilgan Center for Research and Learning, The Hospital for Sick Children
Rishikesan KamaleswaranEmory University School of Medicine
Rishikesan KamaleswaranGeorgia Institute of Technology
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