Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Journal: Disease markers
PMID: 30838085

Abstract

INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before.

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.
  • Jan Budzianowski
    Department of Cardiology, Nowa Sól Multidisciplinary Hospital, Nowa Sól, Poland. jan.budzianowski@gmail.com.
  • Janusz Rzeźniczak
    Department of Cardiology, J. Struś Hospital, Poznań, Poland.
  • Karolina Pieszko
    University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland.
  • Paweł Burchardt
    Department of Biology and Lipid Disorders, Poznań University of Medical Sciences, Poznań, Poland.

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