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Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data

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dc.contributor.advisor Natasha Markuzon and Roy Welsch. en_US
dc.contributor.author Snyder, Ashley M. (Ashley Marie) en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US
dc.date.accessioned 2011-02-23T14:28:25Z
dc.date.available 2011-02-23T14:28:25Z
dc.date.copyright 2010 en_US
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/61199
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 163-166). en_US
dc.description.abstract Data mining is a versatile and expanding field of study. We show the applications and uses of a variety of techniques in two very different realms: Emergency department (ED) length of stay prediction and visual analytics. For the ED, we investigate three data mining techniques to predict a patient's length of stay based solely on the information available at the patient's arrival. We achieve good predictive power using Decision Tree Analysis. Our results show that by using main characteristics about the patient, such as chief complaint, age, time of day of the arrival, and the condition of the ED, we can predict overall patient length of stay to specific hourly ranges with an accuracy of 80%. For visual analytics, we demonstrate how to mathematically determine the optimal number of clusters for a geospatial dataset containing both numeric and categorical data and then how to compare each cluster to the entire dataset as well as consider pairwise differences. We then incorporate our analytical methodology in visual display. Our results show that we can quickly and effectively measure differences between clusters and we can accurately find the optimal number of clusters in non-noisy datasets. en_US
dc.description.statementofresponsibility by Ashley M. Snyder. en_US
dc.format.extent 166 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Operations Research Center. en_US
dc.title Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data en_US
dc.title.alternative Real time predictions and pattern discovery in hospital emergency rooms and immigration data en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 701084313 en_US


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