About: Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units
Creator	Yu, Chao Dong, Yinzhao Ren, Guoqi
source	PMC
abstract	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.
has issue date	2020-07-09(xsd:dateTime)
bibo:doi	10.1186/s12911-020-1120-5
has license	cc-by
sha1sum (hex)	cc6d6898f79a6c95ad9699bc79a48fe261619f41
schema:url	https://doi.org/10.1186/s12911-020-1120-5
resource representing a document's title	Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units
has PubMed Central identifier	PMC7344039
schema:publication	BMC Med Inform Decis Mak
resource representing a document's body	covid:cc6d6898f79a6c95ad9699bc79a48fe261619f41#body_text
is schema:about of	named entity 'terms' named entity 'algorithm' named entity 'term' named entity 'However' named entity 'domains' named entity 'strategy' named entity 'TERMS' named entity 'LONG TERM' named entity 'GOAL' named entity 'DECISION-MAKING' named entity 'PROCESS' named entity 'PATIENT' named entity 'SEEN' named entity 'BACKGROUND' named entity 'BUT' named entity 'patient' named entity 'addressing' named entity 'Intensive Care' named entity 'maximize' named entity 'long' named entity 'utilization' named entity 'Recent' named entity 'therapeutic' named entity 'evaluate' named entity 'ICUs' named entity 'problems' named entity 'ICUs' named entity 'reward function' named entity 'supervised learning' named entity 'algorithm' named entity 'algorithm' named entity 'decision making' named entity 'Intensive Care Units' named entity 'reinforcement learning' named entity 'deviation' named entity 'one-hot' named entity 'supervised learning' named entity 'data set' named entity 'status register' named entity 'supervised learning' named entity 'FiO2' named entity 'sedative' named entity 'positive end-expiratory pressure' named entity 'vital signs' named entity 'ventilator' named entity 'supervised learning' named entity 'neurons' named entity 'ICUs' named entity 'ICU' named entity 'residence time' named entity 'register bit' named entity 'clinical treatment' named entity 'ICUs' named entity 'ICUs' named entity 'sedative' named entity 'ICUs' named entity 'ICUs' named entity 'sedative' named entity 'data set' named entity 'activation function' named entity 'MIMIC' named entity 'clinical treatment' named entity 'ICUs' named entity 'clinical treatment' named entity 'discount factor' named entity 'Massachusetts Institute of Technology' named entity 'partial pressure' named entity 'data processing' named entity 'dimension reduction' named entity 'learning rate' named entity 'physiological' named entity 'ventilator' named entity 'neurons' named entity 'SVM' named entity 'neurons' named entity 'clinical treatment' named entity 'pneumonia' named entity 'conditional entropy' named entity 'one-hot' named entity 'heart disease' named entity 'ICU'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

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 © 2009-2025 OpenLink Software