Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Journal: Journal of the American Heart Association
Published Date: Oct 25, 2024

Abstract

BACKGROUND: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variables, including stroke severity, along with missing data. We developed a natural language processing model that reads electronic health record notes to directly extract the National Institutes of Health Stroke Scale score when documented and predict the score from clinical documentation when missing.

Authors

  • Marta Fernandes
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal. Electronic address: marta.fernandes@tecnico.ulisboa.pt.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
  • Aneesh B Singhal
    Department of Neurology Massachusetts General Hospital (MGH) Boston MA.
  • Sahar F Zafar
    Department of Neurology, Massachusetts General Hospital, Boston, MA.

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