Study developed a machine learning model predicting depressive symptoms in obstructive sleep apnea, indicating effective early identification strategies.
This study developed and externally validated an interpretable random forest-based model to predict the risk of depressive symptoms in patients with OSA. The model integrates key biopsychosocial features, shows good discrimination and calibration, and offers a favorable net benefit. The accompanying web‑based tool supports practical risk assessment and may facilitate early identification, risk stratification, and personalized intervention to help prevent progression to MDD.
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Li et al. (2026) studied this question.
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