To refer to this page use:
|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 Qiteration 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.|
|Citation:||Prasad, Niranjani, Li-Fang Cheng, Corey Chivers, Michael Draugelis, and Barbara E Engelhardt. “A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units.” 33rd Conference on Uncertainty in Artificial Intelligence, 2017.|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||33rd Conference on Uncertainty in Artificial Intelligence|
|Version:||Final published version. This is an open access article.|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.