SleepBert: An Intelligent Clinical Encyclopaedia for Sleep Disorders Using Large Language Models

Diagnosis of sleep disorders is difficult owing to the nature of sleep microarchitecture and the heterogeneity of symptom presentation. Conventional analysis of Polysomnography (PSG)—the interpretation of EEG bandpower, sleep spindles, and K-complexes—is time-consuming, laborious, and subjective, restricting detection of infrequent co-occurrences of disorders and their link to neuro-cognitive and genetic disorders. To overcome these challenges, we present SleepBert , a hybrid Retrieval-Augmented Generation (RAG) model that combines structured PSG features with unstructured clinical narratives for holistic sleep disorder analysis. Constructed by fine-tuning ClinicalBERT on PSG data from the NCH (paediatric dataset) and ISRUC datasets, SleepBert has a PSG-specific knowledge retrieval layer to retrieve real-time evidence from medical databases such as PubMed. The model delivered 93.40% accuracy, outdoing ClinicalBERT (87.20%) and BERT (80.90%), with 90.1% accuracy in retrieving PubMed and response latency of 5.4 seconds. This system serves as an Encyclopaedia of sleep disorders, delivering evidence-based, correct insights and support for decision making to clinicians and researchers. The system supports the analysis of a large number of PSGs, speeds up data-driven discoveries, and allows access to rare neuro-cognitive and genetic markers. SleepBert is an extensible platform for pushing the frontier of sleep disorder research and enhancing clinical decision-making through quick, accurate interpretations of sophisticated PSG data.