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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chausa, P. et al. (2009). Temporal Data Mining of HIV Registries: Results from a 25 Years Follow-Up. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_7
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DOI: https://doi.org/10.1007/978-3-642-02976-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02975-2
Online ISBN: 978-3-642-02976-9
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