A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E Engelhardt
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:41-50, 2017.

Abstract

The management of invasive mechanical venti- lation, and the regulation of sedation and anal- gesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechan- ical ventilation and premature extubation are as- sociated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ven- tilator varies. This work aims to develop a de- cision support tool that uses available patient in- formation to predict time-to-extubation readiness and to recommend a personalized regime of seda- tion dosage and ventilator support. To this end, we use off-policy reinforcement learning algo- rithms to determine the best action at a given pa- tient 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 demon- strate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintu- bation and regulating physiological stability.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-prasad17a, title = {A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units}, author = {Prasad, Niranjani and Cheng, Li-Fang and Chivers, Corey and Draugelis, Michael and Engelhardt, Barbara E}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {41--50}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/prasad17a/prasad17a.pdf}, url = {https://proceedings.mlr.press/r15/prasad17a.html}, abstract = {The management of invasive mechanical venti- lation, and the regulation of sedation and anal- gesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechan- ical ventilation and premature extubation are as- sociated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ven- tilator varies. This work aims to develop a de- cision support tool that uses available patient in- formation to predict time-to-extubation readiness and to recommend a personalized regime of seda- tion dosage and ventilator support. To this end, we use off-policy reinforcement learning algo- rithms to determine the best action at a given pa- tient 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 demon- strate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintu- bation and regulating physiological stability.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units %A Niranjani Prasad %A Li-Fang Cheng %A Corey Chivers %A Michael Draugelis %A Barbara E Engelhardt %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-prasad17a %I PMLR %P 41--50 %U https://proceedings.mlr.press/r15/prasad17a.html %V R15 %X The management of invasive mechanical venti- lation, and the regulation of sedation and anal- gesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechan- ical ventilation and premature extubation are as- sociated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ven- tilator varies. This work aims to develop a de- cision support tool that uses available patient in- formation to predict time-to-extubation readiness and to recommend a personalized regime of seda- tion dosage and ventilator support. To this end, we use off-policy reinforcement learning algo- rithms to determine the best action at a given pa- tient 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 demon- strate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintu- bation and regulating physiological stability. %Z Reissued by PMLR on 04 October 2026.
APA
Prasad, N., Cheng, L., Chivers, C., Draugelis, M. & Engelhardt, B.E.. (2017). A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:41-50 Available from https://proceedings.mlr.press/r15/prasad17a.html. Reissued by PMLR on 04 October 2026.

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