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http://scihub22266oqcxt.onion/ 33691029!?!33691029 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33691029&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Pac+Symp+Biocomput 2021 ; 26 (?): 328-335 Nephropedia Template TP {{tp|p=33691029|t=2021. How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data |pdf=|usr=}}{{33691029}} gab.com Text How Much Does the (Social) Environment Matter Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data JO: Pac Symp Biocomput http://www.kidney.de/pget.php?&tw=1&pmid=33691029 #FOAvip While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths-even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further. Twit Text FOAVip How Much Does the (Social) Environment Matter Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data ????Pac Symp Biocomput http://www.kidney.de/pget.php?&tw=1&pmid=33691029 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33691029 #FOAvip English Wikipedia <ref>{{Cite pmid|33691029}}</ref> How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data #MMPMID33691029 Makridis CA; Mudide A; Alterovitz G Pac Symp Biocomput 2021[]; 26 (?): 328-335 PMID33691029show ga While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths-even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further. |*COVID-19[MESH] |Artificial Intelligence[MESH] |Computational Biology[MESH] |Demography[MESH] |Humans[MESH]How Much Does the (Social) Environment Matter? Using Artificial Intell(epmc).pdf DeepDyve Pubget Overpricing