tetano
Editor, Senior Moderator
J Am Med Inform Assoc
. 2020 Dec 9;ocaa324.
doi: 10.1093/jamia/ocaa324. Online ahead of print.
On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic
Bryan P Bednarski[SUP] 1 [/SUP], Akash Deep Singh[SUP] 1 [/SUP], William M Jones[SUP] 1 [/SUP]
Affiliations
Abstract
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 (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. 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 93-95%. 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.
Conclusion: These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.
Keywords: Allocation; Artificial Intelligence; Coronavirus; Machine Learning; Resource.
. 2020 Dec 9;ocaa324.
doi: 10.1093/jamia/ocaa324. Online ahead of print.
On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic
Bryan P Bednarski[SUP] 1 [/SUP], Akash Deep Singh[SUP] 1 [/SUP], William M Jones[SUP] 1 [/SUP]
Affiliations
- PMID: 33295626
- DOI: 10.1093/jamia/ocaa324
Abstract
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 (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. 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 93-95%. 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.
Conclusion: These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.
Keywords: Allocation; Artificial Intelligence; Coronavirus; Machine Learning; Resource.