Motif Discovery in Physiological Datasets: A Methodology for Inferring Predictive Elements
Name
Guttag_Motif Discovery.pdf
Size
3.99 MB
Format
Adobe PDF
Checksum (MD5)
29dbb3459fbe1b9afab6d36ab74741b7
Author(s)
Syed, Zeeshan
Stultz, Collin M.
Kellis, Manolis
Indyk, Piotr
Guttag, John V.
Date Issued
January 2010
Journal
ACM Transactions on Knowledge Discovery from Data (TKDD)
Publisher
Association for Computing Machinery (ACM)
Citation
Zeeshan Syed, Collin Stultz, Manolis Kellis, Piotr Indyk, and John Guttag. 2010. Motif discovery in physiological datasets: A methodology for inferring predictive elements. ACM Trans. Knowl. Discov. Data 4, 1, Article 2 (January 2010), 23 pages.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1644873.1644875
