Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore
essential to keep making progress in improving the prognosis and quality of life of affected patients. One
way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS—so
that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs).
They allow us to represent the dependencies between a number of diseases and other specific diseases. However,
classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence
generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs
has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a
semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of
annotated training data. Our system has been able to extract a good number of relationships between HIV-related
diseases that have been previously detected in the literature but are scattered and are often little known.
Furthermore, a number of plausible new relationships have shown up which deserve further investigation by
qualified medical experts.