Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

Journal: The journals of gerontology. Series A, Biological sciences and medical sciences
Published Date: Mar 3, 2022

Abstract

BACKGROUND: Delirium 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.

Authors

  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Guilherme S Lopes
    Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
  • Sandeep R Pagali
    Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Bjoerg Thorsteinsdottir
    Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Nathan K LeBrasseur
    Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, Minnesota.
  • Andrew Wen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Walter A Rocca
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Janet E Olson
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
  • Jennifer St Sauver
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.

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