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10.1016/j.eswa.2021.114832 http://scihub22266oqcxt.onion/10.1016/j.eswa.2021.114832 33723478!7948528!33723478     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Expert+Syst+Appl 2021 ; 176 (ä): 114832 Nephropedia Template TP {{tp|p=33723478|t=2021. Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19 |pdf=|usr=}}{{33723478}} gab.com Text Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19 JO: Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33723478 #FOAvip In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. Twit Text FOAVip Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19 ????Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33723478 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33723478 #FOAvip English Wikipedia <ref>{{Cite pmid|33723478}}</ref> Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19 #MMPMID33723478 Smith M; Alvarez F Expert Syst Appl 2021[Aug]; 176 (ä): 114832 PMID33723478show ga In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. äIdentifying mortality factors from Machine Learning using Shapley valu(epmc).pdf DeepDyve Pubget Overpricing