@article{batalMiningRecentTemporal2012,
  title = {Mining {{Recent Temporal Patterns}} for {{Event Detection}} in {{Multivariate Time Series Data}}},
  issn = {2154-817X},
  doi = {10.1145/2339530.2339578},
  abstract = {Improving the performance of classifiers using patternmining tech- niques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our frame- work to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and di- agnosing adverse medical conditions that are associated with dia- betes.},
  journaltitle = {Kdd},
  date = {2012},
  pages = {280--288},
  keywords = {event detection,patient classification,patterns,temporal abstractions,temporal pattern mining,time-interval},
  author = {Batal, Iyad and Harrison, James and Moerchen, Fabian},
  file = {/home/dimitri/Nextcloud/Zotero/storage/V53VW5KD/Batal et al. - Unknown - Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data.pdf},
  eprinttype = {pmid},
  eprint = {25937993}
}

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