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Deep inverse reinforcement learning for sepsis treatment
Chao Yu
, Guoqi Ren
,
Jiming LIU
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
39
Citations (Scopus)
Overview
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Dive into the research topics of 'Deep inverse reinforcement learning for sepsis treatment'. Together they form a unique fingerprint.
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Keyphrases
Sepsis Treatment
100%
Deep Inverse Reinforcement Learning
100%
Reward Function
80%
Sepsis
60%
Optimal Treatment
40%
Effective Treatment
20%
Inconsistency
20%
Learning Problems
20%
Posters
20%
Causes of Mortality
20%
Mini
20%
Paucity
20%
Efficient Strategy
20%
Reinforcement Learning
20%
Tree Model
20%
Reward Learning
20%
Treatment Strategy
20%
Medical Data
20%
Treatment Trajectories
20%
Medical Domain Knowledge
20%
PaO2
20%
Application of Reinforcement Learning
20%
Reinforcement Learning Approach
20%
Pharmacology, Toxicology and Pharmaceutical Science
Sepsis
100%
Learning Disorder
12%
Psychology
Domain Knowledge
100%
Chemical Engineering
Reinforcement Learning
100%