ArticleApplied soft computing2021
Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Diagnosis of Obstructive Sleep Apnea Using Overnight Pulse Oximetry: A Systematic Review and Bayesian Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Nocturnal blood pressure burden: towards a better understanding of the OSA-hypertension relationship.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpOSensors (Basel, Switzerland) · 2025Article
- AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions.Frontiers in digital health · 2025Review
- Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.Sleep medicine · 2024Article
- Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.Nature communications · 2023Article
- Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.Frontiers in neuroscience · 2023Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Overnight pulse oximetry has shown usefulness to simplify obstructive sleep apnea (OSA) diagnosis when combined with machine-learning approaches. However, the development and evaluation of a single model with ability to reach high diagnostic performance in both community-based non-referral and clinical referral cohorts are still pending. Since ensemble-learning algorithms are known for their generalization ability, we propose a least-squares boosting (LSBoost) model aimed at estimating the apnea-hypopnea index (AHI), as the correlate clinical measure of disease severity. A thorough characterization of 8,762 nocturnal blood-oxygen saturation signals (SpO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.