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10.1016/j.smhl.2020.100178 http://scihub22266oqcxt.onion/10.1016/j.smhl.2020.100178 33521226!7832156!33521226     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Smart+Health+(Amst) 2021 ; 20 (ä): 100178 Nephropedia Template TP {{tp|p=33521226|t=2021. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making |pdf=|usr=}}{{33521226}} gab.com Text Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making JO: Smart Health (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=33521226 #FOAvip In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model. Twit Text FOAVip Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making ????Smart Health (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=33521226 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33521226 #FOAvip English Wikipedia <ref>{{Cite pmid|33521226}}</ref> Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making #MMPMID33521226 Pourhomayoun M; Shakibi M Smart Health (Amst) 2021[Apr]; 20 (ä): 100178 PMID33521226show ga In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model. äPredicting mortality risk in patients with COVID-19 using machine lear(epmc).pdf DeepDyve Pubget Overpricing