Temporal characterization of patient state with applications to prediction of tachycardia in anesthesia via induction of inhaled desflurane

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 64-65).It has always been assumed that using clinically measurable parameters is the most efficient way to characterize patient state. By adding additional sensors, monitors, and derived statistics (e.g. mean arterial blood pressure from diastolic and systolic), it was hoped that more information could be garnered about patient state. This thesis challenges the assumption that providing the physician with a full set of clinically measurable parameters is the most efficient way to characterize patient state. The thesis presents a novel way to consider patient state by utilizing reduced dimensionality and by estimating noise. It then explores an application, namely prediction of tachycardia, which often occurs at the onset of induction of inhaled desflurane. One unexpected initial finding was that all 46 patients exhibited tachycardia or hypertension within the first hour of the operation. Three models for predicting tachycardia episodes are proposed, including one model based on use of Blind Noise Adjusted Principal Component Analysis1 (using Iterative Order and Noise Estimate (ION)2 and Principal Component Analysis (PCA)3). Without ION, PCA-based methods alone yielded only 2 useful degrees of freedom, with the rest being relegated to noise. The ION PCA-based method allows one to capture with 5 principal components the information contained in 31 fundamental and derived patient variables, while at the same time reducing the effects of noise. Furthermore, the five discovered significant principal components representing patient state were characterized quantitatively and their physiologic correlates are hypothesized qualitatively. Examination of the 31 original patient parameters in the ION PCA model that predicts tachycardia revealed the relative importance of the original patient parameters to the tachycardia problem. The receiver operating characteristic (ROC) curve for the ION PCA-based predictor suggested a 70% detection rate with 3% false alarms when predicting tachycardia two minutes and twenty seconds into the future. While the patient state characterization method was used for tachycardia prediction, it is potentially useful in myriad medical domains involving multivariate analysis.by Gil Alterovitz.S.M

A Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2006.Includes bibliographical references (leaves 73-85).Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a multi-stage process which compares probabilistic networks generated from mass spectrometry-based data with a mass-based network of protein interactions. In addition, such models can provide insight on features of existing models by identifying relevant proteins. This work finds that the search space of potential proteins can be reduced such that simple antibody-based tests can be used to validate protein identity. This is done with real proteins as a proof of concept. Regarding protein interaction networks, the largest human protein interaction meta-database was created as part of this project, containing over 162,000 interactions. A further contribution is the implementation of the massome network database of mass-based interactions- which is used in the protein identification process.(cont.) This network is explored in terms potential usefulness for protein identification. The framework provides an approach to a number of core issues in proteomics. Besides providing these tools, it yields a novel way to approach statistical signal processing problems in this domain in a way that can be adapted as proteomics-based technologies mature.by Gil Alterovitz.Ph.D