Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1164/rccm.202311-2175LE | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability.
View Article and Find Full Text PDFBackground: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults.
View Article and Find Full Text PDFEvaluation of Dentofacial Angles in Children with Severe Adenoid Hypertrophy.
Iran J Otorhinolaryngol
September 2024
Department of ENT, Golestan University of Medical Science, 5Azar Hospital, Gorgan, Iran.
Introduction: Hypertrophy of adenoids is a common condition in childhood, resulting in obstructive symptoms such as sleep apnea, snoring, and rhinosinusitis. Adenotonsillectomy is recommended to improve prognosis and quality of life. This case-control study compared facial angles and lip position related to dentofacial and mouth growth in symptomatic children with adenoid hypertrophy and asymptomatic control groups.
View Article and Find Full Text PDFHigh-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources.
Can J Stat
September 2024
Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, U.S.A.
When analyzing data combined from multiple sources (e.g., hospitals, studies), the heterogeneity across different sources must be accounted for.
View Article and Find Full Text PDFAdjusting for incomplete baseline covariates in randomized controlled trials: a cross-world imputation framework.
Biometrics
July 2024
Department of Biostatistics, University of Washington, Seattle, WA 98195, United States.
In randomized controlled trials, adjusting for baseline covariates is commonly used to improve the precision of treatment effect estimation. However, covariates often have missing values. Recently, Zhao and Ding studied two simple strategies, the single imputation method and missingness-indicator method (MIM), to handle missing covariates and showed that both methods can provide an efficiency gain compared to not adjusting for covariates.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!