On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic | Zendy

Bryan Bednarski | Zendy; Akash Deep Singh | Zendy; William M. Jones | Zendy

Open Access

On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

Author(s) -

Bryan Bednarski,

Akash Deep Singh,

William M. Jones

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa324

Subject(s) - redistribution (election) , reinforcement learning , computer science , pandemic , covid-19 , inference , artificial intelligence , machine learning , operations research , infectious disease (medical specialty) , disease , engineering , medicine , pathology , politics , political science , law

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.

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