Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression.

Journal: Computers in biology and medicine
Published Date: Sep 14, 2021

Abstract

BACKGROUND AND OBJECTIVES: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

Authors

  • Anđela Blagojević
    Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia.
  • Tijana Šušteršič
    Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia.
  • Ivan Lorencin
    University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia.
  • Sandi Baressi Šegota
    University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000, Rijeka, Croatia. Electronic address: sbaressisegota@riteh.hr.
  • Nikola Anđelić
    University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia. Electronic address: nandelic@riteh.hr.
  • Dragan Milovanović
    Clinical Centre Kragujevac, Zmaj Jovina 30, 34000, Kragujevac, Serbia; University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000, Kragujevac, Serbia. Electronic address: piki@medf.kg.ac.rs.
  • Danijela Baskić
    Clinical Centre Kragujevac, Zmaj Jovina 30, 34000, Kragujevac, Serbia. Electronic address: d.baskic@gmail.com.
  • Dejan Baskić
    University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000, Kragujevac, Serbia; Institute of Public Health Kragujevac, Nikole Pašića 1, 34000, Kragujevac, Serbia. Electronic address: dejan.baskic@gmail.com.
  • Nataša Zdravković Petrović
    Clinical Centre Kragujevac, Zmaj Jovina 30, 34000, Kragujevac, Serbia; University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000, Kragujevac, Serbia. Electronic address: silvester@sbb.com.
  • Predrag Sazdanović
    Clinical Centre Kragujevac, Zmaj Jovina 30, 34000, Kragujevac, Serbia; University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000, Kragujevac, Serbia. Electronic address: predrag.sazdanovic@gmail.com.
  • Zlatan Car
    University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia.
  • Nenad Filipovic

Keywords

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