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10.1016/j.jbi.2021.103743 http://scihub22266oqcxt.onion/10.1016/j.jbi.2021.103743 33753268!8581627!33753268     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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Inform 2021 ; 117 (ä): 103743 Nephropedia Template TP {{tp|p=33753268|t=2021. Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan |pdf=|usr=}}{{33753268}} gab.com Text Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=33753268 #FOAvip Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics. Twit Text FOAVip Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=33753268 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33753268 #FOAvip English Wikipedia <ref>{{Cite pmid|33753268}}</ref> Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan #MMPMID33753268 Rashed EA; Kodera S; Shirakami H; Kawaguchi R; Watanabe K; Hirata A J Biomed Inform 2021[May]; 117 (ä): 103743 PMID33753268show ga Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics. |*Ambulances[MESH] |*COVID-19[MESH] |*Emergency Medical Services[MESH] |*Knowledge Discovery[MESH] |Deep Learning[MESH] |Health Resources[MESH] |Humans[MESH] |Japan[MESH] |Neural Networks, Computer[MESH]Knowledge discovery from emergency ambulance dispatch during COVID-19:(epmc).pdf DeepDyve Pubget Overpricing