Decision-tree and ensemble-based mortality risk models for hospitalized patients with COVID-19

The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of main indicators that lead patients’ mortality. Using good-known parameters are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible understandable decision tree ML ensemble classifiers models would allow physician determine prognosis a few minutes and, accordingly, understand need for treatment adjustment, transfer patient emergency department. accuracy resulting fitted actual hospital data was range 0.88–0.91 different metrics. Creating collection system with further training will dynamically increase forecast’s automate doctor’s decision-making process.