Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

ABSTRACT:

Study objectives

Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality.

Methods

An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts' annotat

SUBMITTER: Liu GR 

PROVIDER: S-EPMC7853209 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature