On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic.
J Am Med Inform Assoc
; 28(4): 874-878, 2021 03 18.
Article
in English
| MEDLINE | ID: covidwho-965544
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 ANDMETHODS:
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Public Health Administration
/
Algorithms
/
Resource Allocation
/
Equipment and Supplies
/
Machine Learning
/
COVID-19
Type of study:
Randomized controlled trials
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Jamia
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