Evaluating parameter optimization in locality-sensitive hashing for high-dimensional physiological waveforms
Name
1088411546-MIT.pdf
Description
Full printable version
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6.61 MB
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Checksum (MD5)
a7e45db9524b89cdf86dae94f668688d
Author(s)
Chakradhar, Vineel A
Advisor(s)
Erik Hemberg.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
We develop and evaluate a theoretical architecture to inform parameter choice for locality-sensitive hashing methods used towards identifying similarity in physiological waveform time-series data. The goal is to achieve increased probability of successful patient outcomes in emergency rooms by tackling the problem of efficient information retrieval within massive, high-dimensional medical datasets. To solve this problem, we explore the relationship between a number of data inputs and elements of locality-sensitive hashing schemes in order to drive optimal choice of parameters throughout the pipeline from raw data to locality-sensitive hashing output. We achieve significant increases in retrieval times while generally maintaining the prediction accuracy achieved by naive retrieval methodologies.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis. "The pagination listed in the Table of Contents does not correlate with actual page numbering"--Disclaimer Notice page.
Includes bibliographical references (pages 71-72).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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