Predicting optimal sedation control with reinforcement learning

Administering sedation to patients to avoid underdosing and overdosing is an important clinical task that remains hard to control due to lack of precision in current methods of measuring sedation. The type of drugs administered, the procedure the patient is undergoing, patient characteristics (age, gender, weight, height), even genotypes can affect the way the patient's body processes the sedation administered. Currently, sedation is administered by an attending anesthesiologist who sets a target sedation level and continuously monitors the patient with an EEG and adjusts the target level accordingly. In this thesis, I apply Fitted Q-Iteration to learn a Reinforcement Learning Model that takes in a patient's current state and predicts the dosage of sedation to administer at each second during the procedure to keep the patient's physiological variables within clinically normal ranges. I experiment with different state and action representations to demonstrate how different choices affect the policy learned by the Reinforcement Learning Model. I evaluate the results qualitatively and quantitatively through the implementation of Doubly Robust Policy Evaluation.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Vajapey, Anuhya.

Advisor dc:contributor.advisor

• Peter Szolovits.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123126

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123126