Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients

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Abstract
Patients presenting to neurology clinics commonly have a complex history of comorbidities and partially documented health trajectories, making it essential to reliably extract comorbidity information from historical records. However, existing extraction methods, ranging from rule-based systems to classical machine learning (ML), often have limited accuracy, scalability, or adaptability across diverse documents. We present a large language model (LLM)–based framework for comorbidity extraction from diagnostic texts, capable of handling various prompt formats and textual sources such as patient history, comorbidities, and sleep assessments. The instruction fine-tuned Mistral-24B (Instruct-2501) model achieves 95% macro classification accuracy and 92% F1 score across six common classes of comorbidities, achieving strong performance that is competitive with metrics reported in prior clinical phenotyping and information extraction studies, while complementing recent transformer-based clinical NLP frameworks. The proposed method extracts comorbidities through a transparent hierarchical approach, thereby supporting clinical analysis and providing interpretable insights for disease modeling and personalized treatment planning in sleep medicine.

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