Early identification of risk factors for obstructive sleep apnea hypopnea syndrome based on large language models.

Background: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a highly prevalent sleep-related breathing disorder, yet it is frequently missed in its early stages. Early identification of OSAHS-related risk factors and early signals from real-world patient-generated text may enable timely screening and intervention. However, existing approaches primarily rely on structured clinical data or end-to-end classification models, which are limited in handling unstructured, colloquial descriptions and highly imbalanced class distributions. Objectives: This study aimed to develop and evaluate a large language model-based framework for early identification of OSAHS-related risk factors and early signals from free-text descriptions, enabling robust text-level risk stratification with improved interpretability. Methods: We proposed OSAHSrisk-LLM, a large language model-based framework that analyzes patient-generated text using a relevance-aware and ontology-constrained reasoning strategy. The framework first determines whether a text contains OSAHS-related risk information, and then applies stepwise, evidence-based reasoning to identify early signals and risk factors under predefined clinical knowledge guidance. Linguistic variability in real-world narratives is addressed by normalizing extracted concepts into standardized clinical terms. We evaluated the framework on a corpus of real-world Chinese text describing sleep-related experiences and compared it with multiple baseline text classification models. Results: OSAHSrisk-LLM achieved an overall accuracy of 92.9% in the four-class text-level classification task, numerically outperforming the baseline models including CNN, Text-CNN, Transformer, and BERT on the current dataset. Notably, the proposed framework demonstrated strong robustness under highly imbalanced class distributions and improved identification of minority categories. Conclusion: Our findings suggest that large language models, when integrated with clinical knowledge constraints and structured reasoning strategies, can effectively extract early OSAHS-related risk information from patient narratives. The proposed framework demonstrates the potential of large language models to identify textual mentions of OSAHS-related risk factors and early signals in patient-generated narratives. Further validation against clinically confirmed OSAHS diagnoses and prospective screening cohorts is required before use in real-world clinical risk screening.