Predicting Mortality and Readmission in Obstructive Sleep Apnea via LLM-Expanded Clinical Concepts

Obstructive Sleep Apnea (OSA) is a common sleep disorder associated with serious health risks. This study leverages large language models (LLMs) to process and interpret clinical narratives in electronic health records. It develops clinically meaningful lexicons for predicting mortality and readmission risk, as well as for multiclass diagnostic classification in OSA patients. Using LLM-expanded lexicons, logistic regression models achieved ROC–AUC scores of 0.844 for 6-month all-cause post-discharge mortality, 0.817 for 1-year all-cause post-discharge mortality, and 0.729 for all-cause hospital readmissions following the first discharge. Diagnostic performance was highest with smaller n-gram representations, indicating that additional contextual length did not improve performance. Compared with frequency-based n-gram models, LLM-expanded lexicons yielded sparser feature sets with lower computational cost and comparable performance. Our findings highlight the potential of LLM-expanded lexicons to enhance OSA diagnosis and clinical risk stratification.