A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability.
verdicts
UNVERDICTED 5representative citing papers
VentAgent uses LLMs in a three-stage Perception-Planning-Orchestration hierarchy to perform multi-objective arbitration for mechanical ventilation in ARDS, outperforming RL baselines on a simulator while producing human-readable reasoning.
Offline RL for ICU sedation shows that adding 30-day mortality to the objective yields policies whose clinician agreement correlates negatively with mortality, unlike pain-only versions.
This survey compiles deep reinforcement learning algorithms for clinical decision support, reviews case studies, and offers guidance on algorithm selection for medical applications.
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.
citing papers explorer
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation
VentAgent uses LLMs in a three-stage Perception-Planning-Orchestration hierarchy to perform multi-objective arbitration for mechanical ventilation in ARDS, outperforming RL baselines on a simulator while producing human-readable reasoning.
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On Safer Reinforcement Learning for Sedation and Analgesia in Intensive Care
Offline RL for ICU sedation shows that adding 30-day mortality to the objective yields policies whose clinician agreement correlates negatively with mortality, unlike pain-only versions.
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Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
This survey compiles deep reinforcement learning algorithms for clinical decision support, reviews case studies, and offers guidance on algorithm selection for medical applications.
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Offline RL promises to extract high-utility policies from static datasets but faces fundamental challenges that current methods only partially address.