An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

ABSTRACT: Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10

SUBMITTER: Sardesai AU 

PROVIDER: S-EPMC8377018 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature