Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study.

Journal: International journal of environmental research and public health
Published Date: Nov 19, 2022

Abstract

BACKGROUND: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2vec word embedding-based model in decreasing the risk of false negatives during the triage of patients with suspected MIS in the emergency department (ED).

Authors

  • Antonio Desai
    Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.
  • Aurora Zumbo
    Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy.
  • Mauro Giordano
    Department of Advanced Medical and Surgical Sciences, University of Campania "L. Vanvitelli", 80138 Naples, Italy.
  • Pierandrea Morandini
    Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, 20089 Milan, Italy.
  • Maria Elena Laino
    Department of Radiology, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.
  • Elena Azzolini
    Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.
  • Andrea Fabbri
    Department of Systems Medicine, University of Rome "Tor Vergata", 00133 Rome, Italy.
  • Simona Marcheselli
    Stroke Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.
  • Alice Giotta Lucifero
    Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy.
  • Sabino Luzzi
    Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy.
  • Antonio Voza
    Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.

Keywords

External Resources

Popular Topics
Recent Journals