Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data
Name
701084313-MIT.pdf
Description
Full printable version
Size
32.94 MB
Format
Adobe PDF
Checksum (MD5)
158a0b00c249f81afb137287b2712e3c
Author(s)
Snyder, Ashley M. (Ashley Marie)
Advisor(s)
Natasha Markuzon and Roy Welsch.
Alternative Title
Real time predictions and pattern discovery in hospital emergency rooms and immigration data
Date Issued
2010
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 163-166).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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