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

Publication ,  Journal Article
Liu, G-R; Lin, T-Y; Wu, H-T; Sheu, Y-C; Liu, C-L; Liu, W-T; Yang, M-C; Ni, Y-L; Chou, K-T; Chen, C-H; Wu, D; Lan, C-C; Chiu, K-L; Chiu, H-Y; Lo, Y-L
Published in: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
February 2021

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.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' annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR.In the intracenter assessment, the median accuracy ranged from 80.3%-83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%-83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital.The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.

Duke Scholars

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Published In

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

DOI

EISSN

1550-9397

ISSN

1550-9389

Publication Date

February 2021

Volume

17

Issue

2

Start / End Page

159 / 166

Related Subject Headings

  • Taiwan
  • Sleep Stages
  • Sleep
  • Reproducibility of Results
  • Neurology & Neurosurgery
  • Machine Learning
  • Humans
  • Artificial Intelligence
  • Algorithms
  • 3202 Clinical sciences
 

Citation

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MLA
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Liu, G.-R., Lin, T.-Y., Wu, H.-T., Sheu, Y.-C., Liu, C.-L., Liu, W.-T., … Lo, Y.-L. (2021). Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 17(2), 159–166. https://doi.org/10.5664/jcsm.8820
Liu, Gi-Ren, Ting-Yu Lin, Hau-Tieng Wu, Yuan-Chung Sheu, Ching-Lung Liu, Wen-Te Liu, Mei-Chen Yang, et al. “Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine 17, no. 2 (February 2021): 159–66. https://doi.org/10.5664/jcsm.8820.
Liu G-R, Lin T-Y, Wu H-T, Sheu Y-C, Liu C-L, Liu W-T, et al. Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2021 Feb;17(2):159–66.
Liu, Gi-Ren, et al. “Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, vol. 17, no. 2, Feb. 2021, pp. 159–66. Epmc, doi:10.5664/jcsm.8820.
Liu G-R, Lin T-Y, Wu H-T, Sheu Y-C, Liu C-L, Liu W-T, Yang M-C, Ni Y-L, Chou K-T, Chen C-H, Wu D, Lan C-C, Chiu K-L, Chiu H-Y, Lo Y-L. Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2021 Feb;17(2):159–166.

Published In

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

DOI

EISSN

1550-9397

ISSN

1550-9389

Publication Date

February 2021

Volume

17

Issue

2

Start / End Page

159 / 166

Related Subject Headings

  • Taiwan
  • Sleep Stages
  • Sleep
  • Reproducibility of Results
  • Neurology & Neurosurgery
  • Machine Learning
  • Humans
  • Artificial Intelligence
  • Algorithms
  • 3202 Clinical sciences