ArticleJournal of medical systems2026
Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients.
Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Autonomous Assessment of Medical Consent Forms: Development and Preliminary Feasibility Demonstration of the Universal Health Communication Index (UHCI).Healthcare (Basel, Switzerland) · 2026Article
- Performance of nine large language models on real-world polysomnography interpretation for obstructive sleep apnea: a multi-dimensional comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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