2026-09-18T22:34:05Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/168272022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Peter Szolovits. Bull, Steven M. (Steven Michael), 1979- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2005-05-19T14:46:04Z 2005-05-19T14:46:04Z 2002 2002 http://hdl.handle.net/1721.1/16827 51072996 Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002. Includes bibliographical references (leaves 67-68). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. This thesis develops a real-time trend detection and monitoring system based on previous work by Haimowitz, Le, and DeSouza [3, 5, 2]. The monitor they designed, TrenDx, used trend templates in which the temporal points where data patterns change are variable with respect to the actual process data. This thesis uses similar models to construct a monitoring system that is able to run in real time, based on a continuous, linearly segmented process data input stream. The instantiation of temporally significant template points against the process data is determined through a simulated annealing algorithm. The rankings of competing hypotheses in the monitor set is based on the distance of these template points from their expected temporal values, along with the area between the process data measurements and the value constraints placed on those parameters. The feasibility of the real-time monitor was evaluated in the domain of pediatric growth, particularly in comparison to previous versions of TrenDx, using an expert gold standard of the diagnoses of pediatric endocrinologists. Real-time TrenDx shows promise in its monitoring abilities and should be evaluated in other domains which are more suited to its continuous data stream input model. by Steven M. Bull. M.Eng. 68 leaves 300277 bytes 300007 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Diagnostic process monitoring with temporally uncertain models Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0fd3ad1d-0095-4823-8728-9338171555f5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Diagnostic process monitoring with temporally uncertain models</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2002</PublicationDate> <Authors> <Author> <DisplayName>Bull, Steven M. (Steven Michael), 1979-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis develops a real-time trend detection and monitoring system based on previous work by Haimowitz, Le, and DeSouza [3, 5, 2]. The monitor they designed, TrenDx, used trend templates in which the temporal points where data patterns change are variable with respect to the actual process data. This thesis uses similar models to construct a monitoring system that is able to run in real time, based on a continuous, linearly segmented process data input stream. The instantiation of temporally significant template points against the process data is determined through a simulated annealing algorithm. The rankings of competing hypotheses in the monitor set is based on the distance of these template points from their expected temporal values, along with the area between the process data measurements and the value constraints placed on those parameters. The feasibility of the real-time monitor was evaluated in the domain of pediatric growth, particularly in comparison to previous versions of TrenDx, using an expert gold standard of the diagnoses of pediatric endocrinologists. Real-time TrenDx shows promise in its monitoring abilities and should be evaluated in other domains which are more suited to its continuous data stream input model.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>