Closed-loop control for cardiopulmonary management and intensive care unit sedation using digital imaging

Cardiopulmonary management;Decision support;Pain assessment;Closed-loop control of sedation;Intensive care unit sedation;Expert systems

Gholami, Behnood ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Cardiopulmonary management;    Decision support;    Pain assessment;    Closed-loop control of sedation;    Intensive care unit sedation;    Expert systems;   

Others  :  https://smartech.gatech.edu/bitstream/1853/34793/1/gholami_behnood_082010_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This dissertation introduces a new problem in the delivery of healthcare, which could result inlower cost and a higher quality of medical care as compared to the current healthcare practice. Inparticular, a framework is developed for sedation and cardiopulmonary management for patientsin the intensive care unit. A method is introduced to automatically detect pain and agitationin nonverbal patients, specifically in sedated patients in the intensive care unit, using their facialexpressions. Furthermore, deterministic as well as probabilistic expert systems are developed tosuggest the appropriate drug dose based on patient sedation level. This framework can be usedto automatically control the level of sedation in the intensive care unit patients via a closed-loopcontrol system. Specifically, video and other physiological variables of a patient can be constantlymonitored by a computer and used as a feedback signal in a closed-loop control architecture. Inaddition, the expert system selects the appropriate drug dose based on the patient's sedation level.In clinical intensive care unit practice sedative/analgesic agents are titrated to achieve a specificlevel of sedation. The level of sedation is currently based on clinical scoring systems. In general,the goal of the clinician is to find the drug dose that maintains the patient at a sedation scorecorresponding to a moderately sedated state. This is typically done empirically, administering adrug dose that usually is in the effective range for most patients, observing the patient's response,and then adjusting the dose accordingly. However, the response of patients to any drug dose isa reflection of the pharmacokinetic and pharmacodynamic properties of the drug and the specificpatient. In this research, we use pharmacokinetic and pharmacodynamic modeling to find anoptimal drug dosing control policy to drive the patient to a desired sedation score.