About: BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in healthcare domains. Recent years have seen a great progress of applying RL in addressing decision-making problems in Intensive Care Units (ICUs). However, since the goal of traditional RL algorithms is to maximize a long-term reward function, exploration in the learning process may have a fatal impact on the patient. As such, a short-term goal should also be considered to keep the patient stable during the treating process. METHODS: We use a Supervised-Actor-Critic (SAC) RL algorithm to address this problem by combining the long-term goal-oriented characteristics of RL with the short-term goal of supervised learning. We evaluate the differences between SAC and traditional Actor-Critic (AC) algorithms in addressing the decision making problems of ventilation and sedative dosing in ICUs. RESULTS: Results show that SAC is much more efficient than the traditional AC algorithm in terms of convergence rate and data utilization. CONCLUSIONS: The SAC algorithm not only aims to cure patients in the long term, but also reduces the degree of deviation from the strategy applied by clinical doctors and thus improves the therapeutic effect.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in healthcare domains. Recent years have seen a great progress of applying RL in addressing decision-making problems in Intensive Care Units (ICUs). However, since the goal of traditional RL algorithms is to maximize a long-term reward function, exploration in the learning process may have a fatal impact on the patient. As such, a short-term goal should also be considered to keep the patient stable during the treating process. METHODS: We use a Supervised-Actor-Critic (SAC) RL algorithm to address this problem by combining the long-term goal-oriented characteristics of RL with the short-term goal of supervised learning. We evaluate the differences between SAC and traditional Actor-Critic (AC) algorithms in addressing the decision making problems of ventilation and sedative dosing in ICUs. RESULTS: Results show that SAC is much more efficient than the traditional AC algorithm in terms of convergence rate and data utilization. CONCLUSIONS: The SAC algorithm not only aims to cure patients in the long term, but also reduces the degree of deviation from the strategy applied by clinical doctors and thus improves the therapeutic effect.
Subject
  • Analysis
  • Reinforcement learning
  • Decision-making
  • Markov models
  • Unsolved problems in neuroscience
  • Belief revision
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software