Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers

By Brett L. Moore, Larry D. Pyeatt, Vivekanand Kulkarni, Periklis Panousis, Kevin A. Padrez, Anthony G. Doufas. 2014

67 citations recorded by the source corpus.

Subfields: Anesthesiology and Pain Medicine, Bioengineering, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Anesthesiology and Pain Medicine

1. Shamim Nemati
2. Pierre Humbert
3. Chao Yu
4. Jiming Liu

In Bioengineering

1. Pierre Humbert

In Cognitive Neuroscience

1. George Dimitri Konidaris
2. Chao Yu
3. Finale Doshi-Velez
4. Philip S. Thomas
5. Shamim Nemati
6. Martha White
7. Sridhar Mahadevan
8. Pierre Humbert

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “They also presented the first clinical in vivo trial for closed-loop control of anesthesia administration using RL on 15 human volunteers [208], [209].”
• Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes (2017, background): “There have been notable efforts made in the administration of anesthesia (Moore et al. 2014) , in personalizing cancer (Tenenbaum et al. ) and HIV therapies (Ernst et al. 2006) and in understanding the causality of macro events in diabetes managment (Merck and Klein-berg 2015) .”
• Learning from an expert (2016, methodology): “Recently, [Moore et al., 2014] have successfully applied Reinforcement Learning to a simple anesthesia model relying on BiS.”
• Optimizing for the Future in Non-Stationary MDPs (2020, background): “In such scenarios, if the automated system is not adapted to take such changes into account, or if it is adapted only after observing such changes, then the system might quickly become sub-optimal, incurring severe loss (Moore et al., 2014).”
• Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes (2016, background): “In medicine, RL is mainly investigated for prognosis, classification and diagnosis by means of big/heterogeneous data collection, fusion and analysis [25]-[29], with fewer reports on treatment studies [30], [31].”
• Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes (2016, background): “There have been notable efforts made in the administration of anesthesia [18], in personalizing cancer [24] and HIV therapies [11] and in understanding the causality of macro events in diabetes managment [16].”