Predicting polysomnographic severity thresholds in children using machine learning.

CONCLUSIONS: Machine learning with oximetry and actigraphy identifies most children needing overnight monitoring as determined by polysomnographic severity of oSDB, supporting a potential resource-conscious screening pathway for children undergoing T&A. IMPACT: We provide proof of principle for the utility of machine learning, oximetry, and actigraphy to screen for severe obstructive sleep apnea syndrome (OSAS) in children.Clinical parameters perform poorly in predicting the severity of OSAS, which is confirmed in the current study.The predictive accuracy for severe OSAS improved by a smaller subset of quantifiable physiologic parameters, such as oximetry.The results of this study support a lower cost, patient-friendly screening pathway to identify children in need of in-hospital observation after surgery. PMID: 32386396 [PubMed - as supplied by publisher]
Source: Pediatric Research - Category: Pediatrics Authors: Tags: Pediatr Res Source Type: research