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10.1093/jamia/ocaa324 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa324 33295626!7799039!33295626     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 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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (4): 874-878 Nephropedia Template TP {{tp|p=33295626|t=2021. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic |pdf=|usr=}}{{33295626}} gab.com Text On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33295626 #FOAvip OBJECTIVE: This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. MATERIALS AND METHODS: The system presented is simulated with disease impact statistics from the Institute of Health Metrics, Centers for Disease Control and Prevention, and Census Bureau. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. RESULTS: The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74 +/- 30.8% in simulations with 5 states to 93.50 +/- 0.003% with 50 states. CONCLUSIONS: These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies. Twit Text FOAVip On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33295626 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33295626 #FOAvip English Wikipedia <ref>{{Cite pmid|33295626}}</ref> On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic #MMPMID33295626 Bednarski BP; Singh AD; Jones WM J Am Med Inform Assoc 2021[Mar]; 28 (4): 874-878 PMID33295626show ga OBJECTIVE: This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. MATERIALS AND METHODS: The system presented is simulated with disease impact statistics from the Institute of Health Metrics, Centers for Disease Control and Prevention, and Census Bureau. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. RESULTS: The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93% to 95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74 +/- 30.8% in simulations with 5 states to 93.50 +/- 0.003% with 50 states. CONCLUSIONS: These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies. |*Algorithms[MESH] |*COVID-19[MESH] |*Machine Learning[MESH] |*Public Health Administration[MESH] |Deep Learning[MESH] |Equipment and Supplies/*supply & distribution[MESH] |Pandemics[MESH]On collaborative reinforcement learning to optimize the redistribution(epmc).pdf DeepDyve Pubget Overpricing