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Data Mining and Knowledge Discovery

, Volume 29, Issue 4, pp 950–975 | Cite as

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

  • Carlos SáezEmail author
  • Pedro Pereira Rodrigues
  • João Gama
  • Montserrat Robles
  • Juan M. García-Gómez
Article

Abstract

Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

Keywords

Data quality Change detection Information theory  Information geometry Visual analytics Biomedical data 

Notes

Acknowledgments

The work by C Sáez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. The authors thank Dr. Gregor Stiglic, from the Univeristy of Maribor, Slovenia, for his support on the NHDS data.

Supplementary material

10618_2014_378_MOESM1_ESM.pdf (90 kb)
Supplementary material 1 (pdf 91 KB)

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Carlos Sáez
    • 1
    • 2
    Email author
  • Pedro Pereira Rodrigues
    • 2
    • 3
  • João Gama
    • 3
  • Montserrat Robles
    • 1
  • Juan M. García-Gómez
    • 1
  1. 1.Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Center for Health Technology and Services Research (CINTESIS), Faculdade de Medicina daUniversidade do PortoPortoPortugal
  3. 3.Laboratório de Inteligência Artificial e Apoio à Decisão (LIAAD)-INESCUniversidade do PortoPortoPortugal

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