An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

ABSTRACT: OBJECTIVES:Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN:Observational cohort study. SETTING:Academic medical center from January 2013 to December 2015. PATIENTS:Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus

SUBMITTER: Nemati S 

PROVIDER: S-EPMC5851825 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature