Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

ABSTRACT:

Objective

The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.

Design

Observational cohort study.

Setting

Twenty-four-bed trauma step-down unit.

Patients

Two thousand one hundred fifty-three patients.

Intervention

Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annot

SUBMITTER: Chen L 

PROVIDER: S-EPMC4911247 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature