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.
Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the intensive care unit, and also due to pain or other variants of noxious stimuli. In this dissertation, we develop a rule-based expert system for cardiopulmonary management and intensive care unit sedation. Furthermore, we use probability theory to quantify uncertainty and to extend the proposed rule-based expert system to deal with more realistic situations.
Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment stem from subjective assessment criteria, rather than quantifiable, measurable data. The relevance vector machine (RVM) classification technique is a Bayesian extension of the support vector machine (SVM) algorithm which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. In this dissertation, we use the RVM classification technique to distinguish pain from non-pain as well as assess pain intensity levels. We also correlate our results with the pain intensity assessed by expert and non-expert human examiners.
Next, we consider facial expression recognition using an unsupervised learning framework. We show that different facial expressions reside on distinct subspaces if the manifold is unfolded. In particular, semi-definite embedding is used to reduce the dimensionality and unfold the manifold of facial images. Next, generalized principal component analysis is used to fit a series of subspaces to the data points and associate each data point to a subspace. Data points that belong to the same subspace are shown to belong to the same facial expression.
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. Examples include the motor activity assessment scale (MAAS), the Richmond agitation-sedation scale (RASS), and the modified Ramsay sedation scale (MRSS). 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. In this research, we use pharmacokinetic and pharmacodynamic modeling to find an optimal drug dosing control policy to drive the patient to a desired MRSS score.
Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation, targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images. (Abstract shortened by UMI.)