Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

ABSTRACT: The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to better understand the immediate immune response to injury and how this might influence important patient outcomes such as multi-organ dysfunction syndrome (MODS). In this study, we have assessed the immune response to trauma in 61 patients at three different post-injury time points (ultra-early (<=1 h), 4-12 h, 48-72 h) and analysed relationships with the development of MODS. We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decr

SUBMITTER: Bravo-Merodio L 

PROVIDER: S-EPMC6923383 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature