2020
DOI: 10.1093/sleep/zsaa056.1174
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1180 The Use Of Natural Language Processing To Extract Data From Psg Sleep Study Reports Using National Vha Electronic Medical Record Data

Abstract: Introduction In 2007, Congress asked the Department of Veteran Affairs to pay closer attention to the incidence of sleep disorders among veterans. We aimed to use natural language processing (NLP), a method that applies algorithms to understand the meaning and structure of sentences within Electronic Health Record (EHR) patient free-text notes, to identify the number of attended polysomnography (PSG) studies conducted in the Veterans Health Administration (VHA) and to evaluate the performance… Show more

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Cited by 2 publications
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“…Clinical interpretation and research of PSG reports could benefit by creating a standardized template for sleep reports. Compared to a previous study with average performance of 80% [ 17 ], the overall performance of the algorithm to extract sleep parameter is greater than 90%. The previous study developed based on a regular expression approach, while we used a more sophisticated algorithm to extract the sleep parameters [ 17 ].…”
Section: Discussionmentioning
confidence: 61%
See 1 more Smart Citation
“…Clinical interpretation and research of PSG reports could benefit by creating a standardized template for sleep reports. Compared to a previous study with average performance of 80% [ 17 ], the overall performance of the algorithm to extract sleep parameter is greater than 90%. The previous study developed based on a regular expression approach, while we used a more sophisticated algorithm to extract the sleep parameters [ 17 ].…”
Section: Discussionmentioning
confidence: 61%
“…There are several other clinical studies using NLP algorithm on the VHA EMR database [ 12 , 13 , 14 , 15 ]. Few studies have reported the use of NLP to convert unstructured data from PSG reports into structured data [ 16 , 17 ]. Investigators used regular expression matching techniques to extract total sleep time (TST) with an accuracy of 80%; however, they did not provide detail on validation information in the published study.…”
Section: Introductionmentioning
confidence: 99%