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Abstract:
HIV patients are treated by administration of combinations of antiretroviral
drugs. The very large number of such combinations makes the manual search for
an effective therapy practically impossible, especially in advanced stages of
the disease. Therapy selection can be supported by statistical methods that
predict the outcomes of candidate therapies. However, these methods are based
on clinical data sets that have highly unbalanced therapy representation.This
paper presents a novel approach that considers each drug belonging to a target
combination therapy as a separate task in a multi-task hierarchical Bayes
setting. The drug-specific models take into account information on all
therapies containing the drug, not just the target therapy. In this way, we can
circumvent the problem of data sparseness pertaining to some target
therapies.The computational validation shows that compared to the most commonly
used approach that provides therapy information in the form of input features,
our model has significantly higher predictive power for therapies with very few
training samples and is at least as powerful for abundant therapies.
