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10.1016/j.mayocpiqo.2020.09.008 http://scihub22266oqcxt.onion/10.1016/j.mayocpiqo.2020.09.008 33043272!7534825!33043272     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       Mayo+Clin+Proc+Innov+Qual+Outcomes 2020 ; 4 (6): 725-732 Nephropedia Template TP {{tp|p=33043272|t=2020. Redesigning COVID-19 Care With Network Medicine and Machine Learning |pdf=|usr=}}{{33043272}} gab.com Text Redesigning COVID-19 Care With Network Medicine and Machine Learning JO: Mayo Clin Proc Innov Qual Outcomes http://www.kidney.de/pget.php?&tw=1&pmid=33043272 #FOAvip Emerging evidence regarding COVID-19 highlights the role of individual resistance and immune function in both susceptibility to infection and severity of disease. Multiple factors influence the response of the human host on exposure to viral pathogens. Influencing an individual's susceptibility to infection are such factors as nutritional status, physical and psychosocial stressors, obesity, protein-calorie malnutrition, emotional resilience, single-nucleotide polymorphisms, environmental toxins including air pollution and firsthand and secondhand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, and availability of nutrient-dense food and empty calories. This review examines the network of interacting cofactors that influence the host-pathogen relationship, which in turn determines one's susceptibility to viral infections like COVID-19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients' risk for development of active infection and to devise a comprehensive approach to prevention and treatment. Twit Text FOAVip Redesigning COVID-19 Care With Network Medicine and Machine Learning ????Mayo Clin Proc Innov Qual Outcomes http://www.kidney.de/pget.php?&tw=1&pmid=33043272 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33043272 #FOAvip English Wikipedia <ref>{{Cite pmid|33043272}}</ref> Redesigning COVID-19 Care With Network Medicine and Machine Learning #MMPMID33043272 Halamka J; Cerrato P; Perlman A Mayo Clin Proc Innov Qual Outcomes 2020[Dec]; 4 (6): 725-732 PMID33043272show ga Emerging evidence regarding COVID-19 highlights the role of individual resistance and immune function in both susceptibility to infection and severity of disease. Multiple factors influence the response of the human host on exposure to viral pathogens. Influencing an individual's susceptibility to infection are such factors as nutritional status, physical and psychosocial stressors, obesity, protein-calorie malnutrition, emotional resilience, single-nucleotide polymorphisms, environmental toxins including air pollution and firsthand and secondhand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, and availability of nutrient-dense food and empty calories. This review examines the network of interacting cofactors that influence the host-pathogen relationship, which in turn determines one's susceptibility to viral infections like COVID-19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients' risk for development of active infection and to devise a comprehensive approach to prevention and treatment. äRedesigning COVID-19 Care With Network Medicine and Machine Learning (epmc).pdf DeepDyve Pubget Overpricing