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This research aims to create a machine learning model that predicts depressive symptoms in patients with obstructive sleep apnea (OSA).

April 25, 2026Open Access

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Development and External Validation of an Interpretable Machine Learning‐Based Prediction Model for Depressive Symptoms in Patients With Obstructive Sleep Apnea: A Multicenter Study

ELEnguang LiChangchun University of Chinese Medicine BGBotang GuoShenzhen Luohu People's Hospital FAFangzhu AiJinzhou Medical University

Key Points

This research aims to create a machine learning model that predicts depressive symptoms in patients with obstructive sleep apnea (OSA).
Developed a random forest-based prediction model.
Integrated biopsychosocial features for risk assessment.
Validated the model externally across multiple centers.
Model demonstrated good discrimination and calibration.
Provided favorable net benefit in risk assessment.
Supported early identification and personalized intervention strategies.

Abstract

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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Development and External Validation of an Interpretable Machine Learning‐Based Prediction Model for Depressive Symptoms in Patients With Obstructive Sleep Apnea: A Multicenter Study

Key Points

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