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10.1111/ijcp.13685 http://scihub22266oqcxt.onion/10.1111/ijcp.13685 32810316!7461008!32810316     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       Int+J+Clin+Pract 2020 ; 74 (12): e13685 Nephropedia Template TP {{tp|p=32810316|t=2020. Prognostic machine learning models for COVID-19 to facilitate decision making |pdf=|usr=}}{{32810316}} gab.com Text Prognostic machine learning models for COVID-19 to facilitate decision making JO: Int J Clin Pract http://www.kidney.de/pget.php?&tw=1&pmid=32810316 #FOAvip An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals is difficult. This can be attributed to the fact that COVID-19 is a novel disease and its pathogenesis is still partially understood. However, machine learning algorithms have the capability to analyse a large number of parameters within a short period of time to identify the predictors of disease outcome. Implementing such an algorithm to predict high-risk individuals during the early stages of infection would be helpful in decision making for clinicians such that irreversible damage could be prevented. Here, we propose recommendations to develop prognostic machine learning models using electronic health records so that a real-time risk score can be developed for COVID-19. Twit Text FOAVip Prognostic machine learning models for COVID-19 to facilitate decision making ????Int J Clin Pract http://www.kidney.de/pget.php?&tw=1&pmid=32810316 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32810316 #FOAvip English Wikipedia <ref>{{Cite pmid|32810316}}</ref> Prognostic machine learning models for COVID-19 to facilitate decision making #MMPMID32810316 Subudhi S; Verma A; Patel AB Int J Clin Pract 2020[Dec]; 74 (12): e13685 PMID32810316show ga An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals is difficult. This can be attributed to the fact that COVID-19 is a novel disease and its pathogenesis is still partially understood. However, machine learning algorithms have the capability to analyse a large number of parameters within a short period of time to identify the predictors of disease outcome. Implementing such an algorithm to predict high-risk individuals during the early stages of infection would be helpful in decision making for clinicians such that irreversible damage could be prevented. Here, we propose recommendations to develop prognostic machine learning models using electronic health records so that a real-time risk score can be developed for COVID-19. |*Algorithms[MESH] |*Clinical Decision-Making[MESH] |*Machine Learning[MESH] |COVID-19/*epidemiology[MESH] |Humans[MESH] |Pandemics[MESH]Prognostic machine learning models for COVID-19 to facilitate decision(epmc).pdf DeepDyve Pubget Overpricing