Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Qianyue Hao"'
Hierarchical Multi-agent Model for Reinforced Medical Resource Allocation with Imperfect Information
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 14:1-27
With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by pu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c33586dfbcab23e5b6d5beeb203c665
https://doi.org/10.1145/3552436
https://doi.org/10.1145/3552436
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::915bc76f895fb724beb088f0070f6a70
https://doi.org/10.1145/3534678.3542679
https://doi.org/10.1145/3534678.3542679
Autor:
Zhenyu Han, Lin Chen, Qianyue Hao, Qiwei He, Budeski, Katherine, Depeng Jin, Fengli Xu, Kun Tang, Yong Li
Publikováno v:
Journal of Global Health; 2023, Vol. 13, p1-14, 14p
Publikováno v:
KDD
Facing the outbreak of COVID-19, shortage in medical resources becomes increasingly outstanding. Therefore, efficient strategies for medical resource allocation are urgently called for. Reinforcement learning (RL) is powerful for decision making, but
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fb9facd5d28a21519d0282b6d4b3cb2
https://doi.org/10.1145/3447548.3467181
https://doi.org/10.1145/3447548.3467181
Publikováno v:
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
KDD
KDD
Facing the worldwide rapid spreading of COVID-19 pandemic, we need to understand its diffusion in the urban environments with heterogeneous population distribution and mobility. However, challenges exist in the choice of proper spatial resolution, in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::742a854e437152783a5bb8fafaeb9765