Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning

ABSTRACT: Abstract

Objective

To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.

Materials and methods

By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked.

Results

The combination of HR and Temp was found to be a minimal feature set yi

SUBMITTER:  

PROVIDER: S-EPMC9566305 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature