Why the study?
Predicting patients at risk of obstructive sleep apnea may be incorporated into clinical screening tools.
Do machine learning models utilizing electronic health record data accurately predict obstructive sleep apnea in adults?
Population
285,292 adults who underwent diagnostic sleep studies with available AHI4% values in Kaiser Permanente Southern California
Comparison
Logistic regression vs random forest vs XGBoost models using EHR data
Design
Retrospective cohort prognostic model development and validation study
Key result
Random forest machine learning models utilizing EHR data demonstrated strong discrimination for predicting OSA (ROC-AUC 0.84) and moderate-severe OSA (ROC-AUC 0.82).
Authors
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May aid OSA detection from routine labs but should not yet change practice; leaves open prospective validation before clinical adoption.**[[1]](https://pmc.ncbi.nlm.nih.
Observational (n=285,292)
Do machine learning models utilizing electronic health record data accurately predict obstructive sleep apnea in adults?
Effect estimate: ROC-AUC 0.84
EHR-based machine learning models, particularly random forests, demonstrate strong potential for predicting obstructive sleep apnea using cardiometabolic risk features.
Hwang et al. (2026) conducted an observational in Obstructive sleep apnea (n=285,292). Machine learning models (Random forest, Logistic regression, XGBoost) was evaluated on Prediction of OSA (AHI≥5) and moderate-severe OSA (AHI≥15) (ROC-AUC 0.84). Random forest machine learning models utilizing EHR data demonstrated strong discrimination for predicting OSA (ROC-AUC 0.84) and moderate-severe OSA (ROC-AUC 0.82).