Closed-loop control for cardiopulmonary management and intensive care unit sedation using digital imaging
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This dissertation introduces a new problem in the delivery of healthcare, which could result in lower cost and a higher quality of medical care as compared to the current healthcare practice. In particular, a framework is developed for sedation and cardiopulmonary management for patients in the intensive care unit. A method is introduced to automatically detect pain and agitation in nonverbal patients, specifically in sedated patients in the intensive care unit, using their facial expressions. Furthermore, deterministic as well as probabilistic expert systems are developed to suggest the appropriate drug dose based on patient sedation level. This framework can be used to automatically control the level of sedation in the intensive care unit patients via a closed-loop control system. Specifically, video and other physiological variables of a patient can be constantly monitored by a computer and used as a feedback signal in a closed-loop control architecture. In addition, 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 specific level 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 score corresponding to a moderately sedated state. This is typically done empirically, administering a drug 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 is a reflection of the pharmacokinetic and pharmacodynamic properties of the drug and the specific patient. In this research, we use pharmacokinetic and pharmacodynamic modeling to find an optimal drug dosing control policy to drive the patient to a desired sedation score.