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10.1093/jamia/ocaa322 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa322 33486527!7928935!33486527     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (4): 733-743 Nephropedia Template TP {{tp|p=33486527|t=2021. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence |pdf=|usr=}}{{33486527}} gab.com Text STAN: spatio-temporal attention network for pandemic prediction using real-world evidence JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33486527 #FOAvip OBJECTIVE: We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model. MATERIALS AND METHODS: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties. RESULTS: STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model. CONCLUSIONS: By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization. Twit Text FOAVip STAN: spatio-temporal attention network for pandemic prediction using real-world evidence ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33486527 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33486527 #FOAvip English Wikipedia <ref>{{Cite pmid|33486527}}</ref> STAN: spatio-temporal attention network for pandemic prediction using real-world evidence #MMPMID33486527 Gao J; Sharma R; Qian C; Glass LM; Spaeder J; Romberg J; Sun J; Xiao C J Am Med Inform Assoc 2021[Mar]; 28 (4): 733-743 PMID33486527show ga OBJECTIVE: We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model. MATERIALS AND METHODS: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties. RESULTS: STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model. CONCLUSIONS: By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization. |*Models, Statistical[MESH] |*Neural Networks, Computer[MESH] |*Pandemics[MESH] |COVID-19/epidemiology[MESH] |Deep Learning[MESH] |Epidemiologic Methods[MESH] |Humans[MESH]STAN: spatio-temporal attention network for pandemic prediction using (epmc).pdf DeepDyve Pubget Overpricing