Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.

Journal: Journal of translational medicine
PMID: 30509300

Abstract

BACKGROUND: Increased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in predicting short-term outcomes of acute coronary syndrome (ACS).

Authors

  • Konrad Pieszko
    Department of Cardiology, Nowa Sól Multidisciplinary Hospital, Nowa Sól, Poland.
  • Jarosław Hiczkiewicz
    Department of Cardiology, Nowa Sól Multidisciplinary Hospital, Nowa Sól, Poland.
  • Paweł Budzianowski
    Department of Engineering, University of Cambridge, Cambridge, UK.
  • Janusz Rzeźniczak
    Department of Cardiology, J. Struś Hospital, Poznań, Poland.
  • Jan Budzianowski
    Department of Cardiology, Nowa Sól Multidisciplinary Hospital, Nowa Sól, Poland. jan.budzianowski@gmail.com.
  • Jerzy Błaszczyński
    Institute of Computing Sciences, Poznań University of Technology, 60-965 Poznań, Poland. Electronic address: jerzy.blaszczynski@cs.put.poznan.pl.
  • Roman Słowiński
    Institute of Computing Sciences, Poznań University of Technology, 60-965 Poznań, Poland; Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland. Electronic address: roman.slowinski@cs.put.poznan.pl.
  • Paweł Burchardt
    Department of Biology and Lipid Disorders, Poznań University of Medical Sciences, Poznań, Poland.

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