Predicting All-Cause Mortality in Patients With Obstructive Sleep Apnea Using Sleep-Related Features: A Machine-Learning Approach

This study has confirmed the potential of sleep-related feature analysis and the LGBM algorithm for evaluating the mortality risk in OSA patients. The developed risk-stratification model offers an efficient and interpretable tool for clinicians that emphasizes the significance of patient-specific autonomic responses in mortality prediction. Incorporating survival analysis further validated the robustness of the model in predicting long-term outcomes.