@article{3033745,
    title = "A Multimodal Approach for the Risk Prediction of Intensive Care and
Mortality in Patients with COVID-19",
    author = "Pezoulas, Vasileios C. and Kourou, Konstantina D. and Papaloukas, Costas and and Triantafyllia, Vassiliki and Lampropoulou, Vicky and Siouti, Eleni and and Papadaki, Maria and Salagianni, Maria and Koukaki, Evangelia and and Rovina, Nikoletta and Koutsoukou, Antonia and Andreakos, Evangelos and and Fotiadis, Dimitrios I.",
    journal = "DIAGNOSTIC ONCOLOGY",
    year = "2022",
    volume = "12",
    number = "1",
    publisher = "MDPI",
    doi = "10.3390/diagnostics12010056",
    keywords = "COVID-19; artificial intelligence; dynamic modeling; risk predictors;
ICU scoring index",
    abstract = "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."
}