2026-09-19T12:58:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1195752026-06-06T00:55:16Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
John Guttag.
Shanmugam, Divy
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-11T20:40:45Z
2018-12-11T20:40:45Z
2018
2018
http://hdl.handle.net/1721.1/119575
1076345253
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).
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.
by Divya Shanmugam.
M.Eng.
49 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A tale of two time series methods : representation learning for improved distance and risk metrics
Representation learning for improved distance and risk metrics
Thesis
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<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.</Abstract>
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