TY - GEN
T1 - Temporal data mining of HIV registries
T2 - 12th Conference on Artificial Intelligence in Medicine, AIME 2009
AU - Chausa, Paloma
AU - Cáceres, César
AU - Sacchi, Lucia
AU - León, Agathe
AU - García, Felipe
AU - Bellazzi, Riccardo
AU - Gómez, Enrique J.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - HIV Data Repository
KW - Rule Discovery
KW - Temporal Abstractions
KW - Temporal Data Mining
UR - http://www.scopus.com/inward/record.url?scp=70350244846&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350244846&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02976-9_7
DO - 10.1007/978-3-642-02976-9_7
M3 - Conference contribution
AN - SCOPUS:70350244846
SN - 3642029752
SN - 9783642029752
VL - 5651 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 60
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 18 July 2009 through 22 July 2009
ER -