ArticleHealthcare (Basel, Switzerland)2022
Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 13 citations in OpenAlex.
- 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
- Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients.Journal of medical systems · 2026Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- The Global Burden of Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 2025Review
- The Association between All-Cause Mortality and Obstructive Sleep Apnea in Adults: A U-Shaped Curve.Annals of the American Thoracic Society · 2025Article
- Age modifies the association between severe sleep apnea and all-cause mortality.Sleep medicine · 2024Article
- Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.Respiratory medicine · 2024Observational
- Effects of COVID-19 on Sleep Services Use and Its Recovery.Nature and science of sleep · 2024Article
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.Healthcare (Basel, Switzerland) · 2023Review
- The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000−30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.