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oai:repisalud.isciii.es:20.500.12105/159242025-04-07T14:49:45Zcom_20.500.12105_15322com_20.500.12105_2051com_20.500.12105_2052col_20.500.12105_16938col_20.500.12105_16963col_20.500.12105_16974col_20.500.12105_19619

00925njm 22002777a 4500 dc Araujo, Lourdes author Martinez-Romo, Juan author Bisbal, Otilia author Sánchez-de-Madariaga, Ricardo author Cohort of the Spanish HIV/AIDS Research Network (CoRIS) author 2022-10-28 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. Sci Rep. 2022 Oct 28;12(1):18208. 10.1038/s41598-022-22695-y 2045-2322 Scientific reports 36307506 http://hdl.handle.net/20.500.12105/15924 Discovering HIV related information by means of association rules and machine learning