Outcomes prediction via time intervals related patterns

Robert Moskovitch, Colin Walsh, Fei Wang, George Hripcsak, Nicholas Tatonetti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

The increasing availability of multivariate temporal data in many domains, such as biomedical, security and more, provides exceptional opportunities for temporal knowledge discovery, classification and prediction, but also challenges. Temporal variables are often sparse and in many domains, such as in biomedical data, they have huge number of variables. In recent decades in the biomedical domain events, such as conditions, drugs and procedures, are stored as time intervals, which enables to discover Time Intervals Related Patterns (TIRPs) and use for classification or prediction. In this study we present a framework for outcome events prediction, called Maitreya, which includes an algorithm for TIRPs discovery called KarmaLegoD, designed to handle huge number of symbols. Three indexing strategies for pairs of symbolic time intervals are proposed and compared, showing that the use of FullyHashed indexing is only slightly slower but consumes minimal memory. We evaluated Maitreya on eight real datasets for the prediction of clinical procedures as outcome events. The use of TIRPs outperform the use of symbols, especially with horizontal support (number of instances) as TIRPs feature representation.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages919-924
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - 2015
Externally publishedYes
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: 14 Nov 201517 Nov 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period14/11/1517/11/15

Keywords

  • Prediction
  • Time Intervals Mining

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