A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19
Vasileios C. Pezoulas,
Konstantina D. Kourou,
Costas Papaloukas,
Vassiliki Triantafyllia,
Vicky Lampropoulou,
Eleni Siouti,
Maria Papadaki,
Maria Salagianni,
Evangelia Koukaki,
Nikoletta Rovina,
Antonia Koutsoukou,
Evangelos Andreakos,
Dimitrios I. Fotiadis
Affiliations
Vasileios C. Pezoulas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece
Konstantina D. Kourou
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece
Costas Papaloukas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece
Vassiliki Triantafyllia
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Vicky Lampropoulou
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Eleni Siouti
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Maria Papadaki
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Maria Salagianni
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Evangelia Koukaki
Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece
Nikoletta Rovina
Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece
Antonia Koutsoukou
Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece
Evangelos Andreakos
Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece
Dimitrios I. Fotiadis
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece
Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers. Methods: We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index. Results: Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9. Conclusions: to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints.
