Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.

Journal: Journal of medical Internet research
Published Date: Mar 8, 2021

Abstract

BACKGROUND: Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events.

Authors

  • Yiqing Zhao
    Department of Health Informatics and Administration, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, 2025 E Newport Ave, NWQ-B Room 6469, Milwaukee, WI, 53211, USA.
  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Suzette J Bielinski
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA. bielinski.suzette@mayo.edu.
  • Paul A Decker
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
  • Alanna M Chamberlain
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • Véronique L Roger
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Nicholas B Larson
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.

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