Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

Journal: BMC medical research methodology
PMID: 36564710

Abstract

BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation.

Authors

  • Marsa Gholamzadeh
    Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, No #17, 5th Floor, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran. Electronic address: m-gholamzadeh@razi.tums.ac.ir.
  • Hamidreza Abtahi
    Pulmonary and Critical Care Medicine Department, Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
  • Reza Safdari
    Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: rsafdari@tums.ac.ir.

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