Sedation of Simulated Icu Patients Using Reinforcement Learning Based Control

By E. Sinzinger, Brett L. Moore. 2005

10 citations recorded by the source corpus.

Subfields: Anesthesiology and Pain Medicine, Critical Care and Intensive Care Medicine, Developmental 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. Chao Yu
3. Adam E. Gaweda
4. Jiming Liu

In Critical Care and Intensive Care Medicine

1. Shamim Nemati
2. Jiming Liu
3. Chao Yu
4. Adam E. Gaweda

In Developmental Neuroscience

1. Shamim Nemati
2. Chao Yu
3. Adam E. Gaweda
4. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “significantly reducing mortality from 49% to 0:8%. Anesthesia Regulation and automation of sedation and analgesia to maintain physiological stability and lowering pains of patients Moore et al. [206], [207] Q() N/A N/A PK/PD model Achieving superior stability compared to a well-tuned PID controller. ”
• Improving management of Anemia in End Stage Renal Disease using Reinforcement Learning (2009, methodology): “In the first reported application of RL to drug dosing [ 9 ], RL-based adaptive controller was shown to perform better than a conventional closed-loop approach, such as PID, when applied to automatic regulation of the sedation level in an intensive care unit.”