Learning deep patient representations for the teleICU
Name
1145124778-MIT.pdf
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728.6 KB
Format
Adobe PDF
Checksum (MD5)
47f1e8fcc915055abc1ac1d4d287fa7c
Author(s)
Oguntola, Ini(Iniokuwa A.)
Advisor(s)
Amar Gupta.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
This thesis presents a method of extracting deep robust representations of teleICU clinical data using Transformer networks, inspired by recent machine learning literature in language modeling. The utility of these representations is evaluated in various prediction outcome tasks, in which they were able to outperform linear and neural baselines. Also examined are the probability distributions of various patient characteristics across the learned patient representation space; where corresponding high-level spatial structure suggests potential for use as a similarity metric or in combination with other patient similarity metrics. Finally, the code for the models developed is publicly provided as a starting point for further research.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 89-93).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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