ArticleChest2022
Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples.
Article in Chest, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine learning identification of neuroimaging signatures in young and middle-aged male patients with obstructive sleep apnea: a multimodal MRI study.Sleep & breathing = Schlaf & Atmung · 2026Article
- Obstructive Sleep Apnea in Critically Ill Patients: A Structured Narrative Review of Prevalence, Diagnostic Barriers, and Clinical Implications in the ICU.Clocks & sleep · 2026Review
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Development and Validation of an Explainable Machine Learning Model for Identifying Hypertension Status in Patients With Obstructive Sleep Apnea.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- Predicting the Apnea-Hypopnea index for the diagnosis of obstructive sleep apnea with cardiovascular and cancer comorbidity using wearable sleep-tracking devices.Sleep & breathing = Schlaf & Atmung · 2026Article
- Machine learning optimization of obstructive sleep apnea screening: development and validation of a gradient boosting prediction model with a clinical implementation framework.Frontiers in medicine · 2026Article
- A Nomogram Model for Predicting Moderate to Severe OSA in Western China: A Retrospective Analysis.Nature and science of sleep · 2026Article
- Integrating Complete Blood Count Parameters with Demographic Characteristics for Obstructive Sleep Apnea Prediction in Chinese Adults: A Machine Learning Approach.Nature and science of sleep · 2026Article
- Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.Frontiers in sleep · 2026Review
- A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data.Singapore medical journal · 2025Article
- Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review.Computational and structural biotechnology journal · 2025Review
- Exploring the complexity of obstructive sleep apnea: findings from machine learning on diagnosis and predictive capacity of individual factors.Sleep & breathing = Schlaf & Atmung · 2024Article
- Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.Sleep & breathing = Schlaf & Atmung · 2024Article
- Article
- Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review.Journal of clinical medicine · 2024Review
- Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction.Plastic and reconstructive surgery. Global open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 11 institutions in 6 countries.
Funding
Abstract
backgroundPrediction tools without patient-reported symptoms could facilitate widespread identification of OSA. RESEARCH QUESTION: What is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples? STUDY DESIGN AND
methodsLogistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were randomly split into training (n = 10,469) and validation (n = 6,979) sets. Model comparisons were performed by using the area under the receiver-operating curve (AUC). Trained models were compared with the STOP-BANG questionnaire in two prospective testing datasets: an independent clinic-based sample from SAGIC (n = 1,613) and a community-based sample from the Sleep Heart Health Study (n = 5,599).
resultsThe AUCs (95% CI) of the machine learning models were significantly higher than logistic regression (0.61 [0.60-0.62]) in both the training and validation datasets (ANN, 0.68 [0.66-0.69]; RF, 0.68 [0.67-0.70]; and kernel support vector machine, 0.66 [0.65-0.67]). In the SAGIC testing sample, the ANN (0.70 [0.68-0.72]) and RF (0.70 [0.68-0.73]) models had AUCs similar to those of the STOP-BANG (0.71 [0.68-0.72]). In the Sleep Heart Health Study testing sample, the ANN (0.72 [0.71-0.74]) had AUCs similar to those of STOP-BANG (0.72 [0.70-0.73]).
interpretationOSA prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms. Machine learning-derived algorithms may have utility for widespread identification of OSA.
Indexed as
Identifiers
What OpenQuestion holds
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.