Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

ABSTRACT:

Objective

Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.

Materials and methods

Retrospective data was drawn from databases at the University of California, San Francisco (UCSF) Medical Center and the Beth Israel Deaconess Medical Center (BIDMC). Adult patient encounters without sepsis on admission, and with at least one recording of each of six vital signs (SpO₂, heart rate, respiratory rate, temperature, systolic and diastolic blood pressure) were included. We compared the performance of the

SUBMITTER: Barton C 

PROVIDER: S-EPMC6556419 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature