• J. Gerontol. A Biol. Sci. Med. Sci. · Oct 2020

    Ascertainment of delirium status using natural language processing from electronic health records.

    • Sunyang Fu, Guilherme S Lopes, Sandeep R Pagali, Bjoerg Thorsteinsdottir, Nathan K LeBrasseur, Andrew Wen, Hongfang Liu, Walter A Rocca, Janet E Olson, Jennifer St Sauver, and Sunghwan Sohn.
    • Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
    • J. Gerontol. A Biol. Sci. Med. Sci. 2020 Oct 30.

    BackgroundDelirium is underdiagnosed in clinical practice, and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium, however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs.MethodsThis study used a randomly selected cohort from the population-based Mayo Clinic Biobank (n=300, age>=65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria; one based on the original CAM (NLP-CAM; delirium vs. no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using ICD-9, NLP-CAM, and NLP-mCAM.ResultsNLP-CAM demonstrated a sensitivity, specificity and accuracy of 0.919, 1.000 and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity and accuracy of 0.827, 0.913 and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12,651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20,611 (15.3%) definite delirium cases and 10,762 (8.0%) possible cases.© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…