A tale of two time series methods : representation learning for improved distance and risk metrics
Name
1076345253-MIT.pdf
Description
Full printable version
Size
1.43 MB
Format
Adobe PDF
Checksum (MD5)
cc76332d767a0968b116e7fde3036ca8
Author(s)
Shanmugam, Divy
Advisor(s)
John Guttag.
Alternative Title
Representation learning for improved distance and risk metrics
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we present methods in representation learning for time series in two areas: metric learning and risk stratification. We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy, a simple and scalable distance metric learning method for multivariate time series. Our approach is to reframe the task as a representation learning problem -- rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective. Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods. We then focus on risk stratification because of its clinical importance in identifying patients at high risk for an adverse outcome. We use segments of a patient's ECG signal to predict that patient's risk of cardiovascular death within 90 days. In contrast to other work, we work directly with the raw ECG signal to learn a representation with predictive power. Our method produces a risk metric for cardiovascular death with state-of-the-art performance when compared to methods that rely on expert-designed representations.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 43-49).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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