Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

ABSTRACT: Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers

SUBMITTER: Taneja I 

PROVIDER: S-EPMC5589821 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature