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

Gi-Ren Liu | Zendy; TingYu Lin | Zendy; HauTieng Wu | Zendy; Yuan-Chung Sheu | Zendy; ChingLung Liu | Zendy; WenTe Liu | Zendy; MeiChen Yang | Zendy; Yung-Lun Ni | Zendy; KunTa Chou | Zendy; ChaoHsien Chen | Zendy; Dean Wu | Zendy; Chou-Chin Lan | Zendy; Kuo-Liang Chiu | Zendy; HwaYen Chiu | Zendy; Yu-Lun Lo | Zendy

Open Access

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

Author(s) -

Gi-Ren Liu,

TingYu Lin,

HauTieng Wu,

Yuan-Chung Sheu,

ChingLung Liu,

WenTe Liu,

MeiChen Yang,

Yung-Lun Ni,

KunTa Chou,

ChaoHsien Chen,

Dean Wu,

Chou-Chin Lan,

Kuo-Liang Chiu,

HwaYen Chiu,

Yu-Lun Lo

Publication year - 2020

Publication title -

journal of clinical sleep medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.529

H-Index - 92

eISSN - 1550-9397

pISSN - 1550-9389

DOI - 10.5664/jcsm.8820

Subject(s) - polysomnography , medicine , sleep (system call) , reliability (semiconductor) , inter rater reliability , kappa , sleep stages , sleep medicine , consistency (knowledge bases) , artificial intelligence , gold standard (test) , machine learning , computer science , rating scale , statistics , apnea , sleep disorder , insomnia , psychiatry , mathematics , power (physics) , physics , quantum mechanics , operating system , geometry

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.

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