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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages56-60
Number of pages5
Volume5651 LNAI
DOIs
Publication statusPublished - 2009
Event12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy
Duration: Jul 18 2009Jul 22 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5651 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th Conference on Artificial Intelligence in Medicine, AIME 2009
CountryItaly
CityVerona
Period7/18/097/22/09

Keywords

  • HIV Data Repository
  • Rule Discovery
  • Temporal Abstractions
  • Temporal Data Mining

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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    Chausa, P., Cáceres, C., Sacchi, L., León, A., García, F., Bellazzi, R., & Gómez, E. J. (2009). Temporal data mining of HIV registries: Results from a 25 years follow-up. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5651 LNAI, pp. 56-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI). https://doi.org/10.1007/978-3-642-02976-9_7