ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- 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
- Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Using Open-Source Large Language Models to Identify Access to Germline Genetic Testing in Veterans With Breast Cancer From Unstructured Text.JCO clinical cancer informatics · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Generative artificial intelligence utilizing transformer technology is widely seen as a groundbreaking advancement in applied artificial intelligence. The technology creates a unique opportunity to extract unstructured data from medical notes. In the current experiments, we extracted fundamental sleep parameters from polysomnography notes of veterans in the Corporate Data Warehouse national database using large language models. The "SOLAR-10.7B-Instruct" model extracted values associated with total sleep time, sleep onset latency, and sleep efficiency from the polysomnography notes. The model's performance was evaluated using 464 human annotated notes. The analysis showed close accuracy for the large language model compared to the human total sleep time and sleep efficiency extraction, and a considerable accuracy improvement (7.6%) in extracting sleep onset latency for the machine compared to human annotation. The large language model shows negligible hallucination (no more than 3.6%), and it has the capability to perform complicated reasoning to extract the desired sleep parameter. CITATION: Maghsoudi A, Sharafkhaneh A, Azarian M, Ramezani A, Hirshkowitz M, Razjouyan J. A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.
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Registered trials
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