A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

ABSTRACT: Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, p

SUBMITTER: Yee CR 

PROVIDER: S-EPMC6829643 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature