A framework for mining signatures from event sequences and its applications in healthcare data. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • February 1, 2013

Research

keywords

  • Artificial Intelligence
  • Data Mining
  • Decision Support Systems, Clinical
  • Decision Support Techniques
  • Electronic Health Records
  • Health Records, Personal
  • Pattern Recognition, Automated

Identity

Scopus Document Identifier

  • 84871741964

Digital Object Identifier (DOI)

  • 10.1109/TPAMI.2012.111

PubMed ID

  • 22585098

Additional Document Info

volume

  • 35

issue

  • 2