Data-driven explainable machine learning for personalized risk classification of myasthenic crisis.

Journal: International journal of medical informatics
PMID: 39566349

Abstract

OBJECTIVE: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring intensive care treatment and invasive therapies. Classifying patients at high-risk for MC facilitates treatment decisions such as changes in medication or the need for mechanical ventilation and helps prevent disease progression by decreasing treatment-induced stress on the patient. Here, we investigated whether it is possible to reliably classify MG patients into groups at low or high risk of MC based entirely on routine medical data using explainable machine learning (ML).

Authors

  • Sivan Bershan
    Charité - Universitätsmedizin Berlin, Center for Stroke Research Berlin, Berlin, Germany.
  • Andreas Meisel
    Department of Neurology, University Medicine Berlin (Charité), Berlin, Germany.
  • Philipp Mergenthaler
    Biological Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada.

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