A framework for mining signatures from event sequences and its applications in healthcare data

Fei Wang, Noah Lee, Jianying Hu, Jimeng Sun, Shahram Ebadollahi, Andrew F. Laine

Research output: Contribution to journalArticlepeer-review


This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the β-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset.

Original languageEnglish (US)
Article number6200289
Pages (from-to)272-285
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number2
StatePublished - Jan 7 2013
Externally publishedYes


  • beta-divergence
  • dictionary learning
  • nonnegative matrix factorization
  • sparse coding
  • stochastic gradient descent
  • Temporal signature mining

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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