A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY.

"Conflicts of interest: R.K., M.R.A., and Cincinnati Children's Hospital Medical Center hold a provisional patent for the machine learning–driven classifier predictive of innate immune endotypes of sepsis. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest."

"Funding: R.K. and M.H. are supported by the National Institutes of Health under Award Numbers R01GM139967 and UL1TR002378. M.R.A. received funding through award R21GM150093. M.R.A. and R.K. received funding through award R21GM151703. Dr Holder is supported by the National Institute of General Medical Sciences of the National Institutes of Health (K23GM37182). He receives speaker fees from Baxter International and has received consultation fees from Philips. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."