Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.
Mohammad M BanoeiRoshan DinparastisalehAli Vaeli ZadehMehdi MirsaeidiPublished in: Critical care (London, England) (2021)
An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
Keyphrases
- coronavirus disease
- machine learning
- sars cov
- cardiovascular events
- end stage renal disease
- risk factors
- chronic kidney disease
- newly diagnosed
- artificial intelligence
- healthcare
- palliative care
- respiratory syndrome coronavirus
- prognostic factors
- big data
- cardiovascular disease
- deep learning
- coronary artery disease
- mass spectrometry
- type diabetes
- patient reported outcomes