Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports

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: Us...

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Bibliographic Details
Published in:Healthcare (Basel) 2022-09, Vol.10 (10), p.1837
Main Authors: Rahman, Mahbubur, Nowakowski, Sara, Agrawal, Ritwick, Naik, Aanand, Sharafkhaneh, Amir, Razjouyan, Javad
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computational linguistics
Data mining
Data warehouses
Diagnosis
Efficiency
Eye movements
Language
Language processing
Methods
Natural language interfaces
Natural language processing
Patients
Physiology
polysomnography
Sleep disorders
sleep parameters
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Summary: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.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare10101837