Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification.

Journal: Computers in biology and medicine
Published Date: Nov 11, 2023

Abstract

BACKGROUND: Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients.

Authors

  • Seyed-Ali Sadegh-Zadeh
    Department of Computing, School of Digital, Technologies and Arts, Staffordshire University Stoke-on-Trent ST4 2DE, UK.
  • Hanie Sakha
    Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, England, United Kingdom.
  • Sobhan Movahedi
    Azad University, Science and Research, Tehran, Iran.
  • Aniseh Fasihi Harandi
    Azad University, Science and Research, Tehran, Iran.
  • Samad Ghaffari
    Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Elnaz Javanshir
    Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Syed Ahsan Ali
    Health Education England West Midlands, Birmingham, England, United Kingdom.
  • Zahra Hooshanginezhad
    Department of Cardiovascular Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Reza Hajizadeh
    Department of Cardiology, Urmia University of Medical Sciences, Urmia, Iran. Electronic address: hajizadh.reza@gmail.com.

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