[HTML][HTML] An interpretable mortality prediction model for COVID-19 patients
… of mortality risk. A mathematical modelling approach based on state-of-the-art … machine
learning algorithms was devised to identify the most discriminative biomarkers of patient mortality…
learning algorithms was devised to identify the most discriminative biomarkers of patient mortality…
[HTML][HTML] Predicting risk of mortality in COVID-19 hospitalized patients using hybrid machine learning algorithms
MR Afrash, M Shanbehzadeh… - Journal of biomedical …, 2022 - ncbi.nlm.nih.gov
… with some ML algorithms to predict COVID-19 mortality at the … the mortality of COVID-19
were determined using the GA optimization procedures and also included in four ML algorithms K…
were determined using the GA optimization procedures and also included in four ML algorithms K…
[HTML][HTML] Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran
S Moslehi, N Rabiei, AR Soltanian… - BMC Medical Informatics …, 2022 - Springer
… mortality and take appropriate action urgently. In this study, the random forest method was
used to select the effective features in COVID-19 mortality and … machine learning algorithm for …
used to select the effective features in COVID-19 mortality and … machine learning algorithm for …
[HTML][HTML] Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
… develop a deep-learning algorithm (… mortality in COVID-19 patients. We further developed
a simplified risk-score model to predict the likelihood of ICU admission and in-hospital mortality…
a simplified risk-score model to predict the likelihood of ICU admission and in-hospital mortality…
[HTML][HTML] Machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in COVID-19
… Machine Learning Algorithms In this study, we have used six clinically-applicable
supervised machine learning algorithms that were applied to identify the minimum number of …
supervised machine learning algorithms that were applied to identify the minimum number of …
[HTML][HTML] COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach
… approach as a robust hybrid algorithm for generating a platform for predicting the COVID-… ,
respectively for the prediction of COVID-19 cases and COVID-19 mortality rate in the presence …
respectively for the prediction of COVID-19 cases and COVID-19 mortality rate in the presence …
Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the …
…, M Vinceti, R De Caterina, L Iacoviello, T COvid - Nutrition, Metabolism …, 2020 - Elsevier
… and their relative importance in influencing mortality risk, followed by a classical … in
COVID-19 patients and their efficacy as prognostic factors in a single machine learning algorithm. …
COVID-19 patients and their efficacy as prognostic factors in a single machine learning algorithm. …
[HTML][HTML] Mortality predictors in patients with COVID-19 pneumonia: A machine learning approach using eXtreme Gradient Boosting model
N Casillas, AM Torres, M Moret, A Gómez… - Internal and Emergency …, 2022 - Springer
… the usefulness of new machine learning algorithms in the search for … Machine Learning
allows us to predict the mortality of the population of patients with Pneumonia due to COVID-19 in …
allows us to predict the mortality of the population of patients with Pneumonia due to COVID-19 in …
[HTML][HTML] Computational intelligence-based model for mortality rate prediction in COVID-19 patients
… COVID-19 cases can reduce the mortality rate and the likelihood of further complications.
Machine Learning (ML) and Deep Learning (… This study used ML algorithms, such as Decision …
Machine Learning (ML) and Deep Learning (… This study used ML algorithms, such as Decision …
[HTML][HTML] Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features
… SSB carried out deep learning and algorithm development, with feedback from SAASNSA,
AT, SI, FS, and SE carried out the investigation. FS, AT, SAASN, and SI wrote the original draft …
AT, SI, FS, and SE carried out the investigation. FS, AT, SAASN, and SI wrote the original draft …