Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 40254242

Abstract

OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individual. In this study, we aimed to use machine learning to identify characteristics influencing the likelihood of not benefiting from successful recanalization.

Authors

  • Vahid Farmani
    Galway Medical Technology Centre, Department of Mechanical and Industrial Engineering, Atlantic Technological University, Galway, Ireland. Electronic address: vahid.farmani@research.atu.ie.
  • Helge Kniep
  • Máté E Maros
    Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, Germany.
  • Olga Lyashevska
    Marine and Freshwater Research Centre, Department of Natural Resources & the Environment, School of Science and Computing, Atlantic Technological University, Galway, Ireland; Netherlands eScience Center, Amsterdam, Netherlands. Electronic address: olga.lyashevska@atu.ie.
  • Fiona Malone
    Galway Medical Technology Centre, Department of Mechanical and Industrial Engineering, Atlantic Technological University, Galway, Ireland. Electronic address: fiona.malone@atu.ie.
  • Jens Fiehler
    Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Liam Morris
    Galway Medical Technology Centre, Department of Mechanical and Industrial Engineering, Atlantic Technological University, Galway, Ireland. Electronic address: liam.morris@atu.ie.

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