Temporal Data Mining of HIV Registries: Results from a 25 Years Follow-Up

  • Paloma Chausa
  • César Cáceres
  • Lucia Sacchi
  • Agathe León
  • Felipe García
  • Riccardo Bellazzi
  • Enrique J. Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

The Human Immunodeficiency Virus (HIV) causes a pandemic infection in humans, with millions of people infected every year. Although the Highly Active Antiretroviral Therapy reduced the number of AIDS cases since 1996 by significantly increasing the disease-free survival time, the therapy failure rate is still high due to HIV treatment complexity. To better understand the changes in the outcomes of HIV patients we have applied temporal data mining techniques to the analysis of the data collected since 1981 by the Infectious Diseases Unit of the Hospital Clínic in Barcelona, Spain. We run a precedence temporal rule extraction algorithm on three different temporal periods, looking for two types of treatment failures: viral failure and toxic failure, corresponding to events of clinical interest to assess the treatment outcomes. The analysis allowed to extract different typical patterns related to each period and to meaningfully interpret the previous and current behaviour of HIV therapy.

Keywords

Temporal Data Mining HIV Data Repository Temporal Abstractions Rule Discovery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paloma Chausa
    • 1
    • 2
  • César Cáceres
    • 1
    • 2
  • Lucia Sacchi
    • 3
  • Agathe León
    • 4
  • Felipe García
    • 4
  • Riccardo Bellazzi
    • 3
  • Enrique J. Gómez
    • 2
    • 1
  1. 1.Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
  2. 2.Bioengineering and Telemedicine GroupPolytechnic University of MadridSpain
  3. 3.Department of Computer Engineering and Systems SciencesUniversity of PaviaItaly
  4. 4.Infectious Diseases Unit, Hospital Clínic of BarcelonaSpain

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