Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

ABSTRACT:

Objectives

We validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings.

Design

A machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time.

Setting

A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachuse

SUBMITTER: Mao Q 

PROVIDER: S-EPMC5829820 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature