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Motif discovery in physiological datasets: A methodology for inferring predictive elements

Content Provider	ACM Digital Library
Author	Guttag, John Kellis, Manolis Stultz, Collin Syed, Zeeshan Indyk, Piotr
Copyright Year	2010
Abstract	In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to efficiently search for such patterns in large datasets. This involves first transforming continuous physiological signals from patients into symbolic sequences, and then searching for patterns in these reduced representations that are strongly associated with an outcome. Our strategy of identifying conserved activity that is unlikely to have occurred purely by chance in symbolic data is analogous to the discovery of regulatory motifs in genomic datasets. We build upon existing work in this area, generalizing the notion of a regulatory motif and enhancing current techniques to operate robustly on non-genomic data. We also address two significant considerations associated with motif discovery in general: computational efficiency and robustness in the presence of degeneracy and noise. To deal with these issues, we introduce the concept of active regions and new subset-based techniques such as a two-layer Gibbs sampling algorithm. These extensions allow for a framework for information inference, where precursors are identified as approximately conserved activity of arbitrary complexity preceding multiple occurrences of an event. We evaluated our solution on a population of patients who experienced sudden cardiac death and attempted to discover electrocardiographic activity that may be associated with the endpoint of death. To assess the predictive patterns discovered, we compared likelihood scores for motifs in the sudden death population against control populations of normal individuals and those with non-fatal supraventricular arrhythmias. Our results suggest that predictive motif discovery may be able to identify clinically relevant information even in the absence of significant prior knowledge.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/1644873.1644875
Volume Number	4
Issue Number	1
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-01-18
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gibbs sampling Data mining Inference Knowledge discovery Motifs Physiological signals
Content Type	Text
Resource Type	Article
Subject	Computer Science

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Motif discovery in physiological datasets: a methodology for inferring predictive elements.

Guttag: Motif discovery in physiological datasets: A methodology for inferring predictive elements (2010)

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Motif discovery in physiological datasets: A methodology for inferring predictive elements

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Motif discovery in physiological datasets: A methodology for inferring predictive elements