2026-05-20T04:01:58Zhttps://repisalud.isciii.es/rest/oai/requestoai: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
Repisalud
author
Araujo, Lourdes
author
Martinez-Romo, Juan
author
Bisbal, Otilia
author
Sánchez-de-Madariaga, Ricardo
author
Cohort of the Spanish HIV/AIDS Research Network (CoRIS)
funder
Ministerio de Ciencia e Innovación (España)
funder
Instituto de Salud Carlos III
funder
Plan Nacional de I+D+i (España)
funder
Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
funder
Red de Investigación Cooperativa en Investigación en Sida (España)
2023-04-27T11:48:23Z
2023-04-27T11:48:23Z
2022-10-28
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
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.
eng
Discovering HIV related information by means of association rules and machine learning
research article
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URL
https://repisalud.isciii.es/bitstreams/90fb023d-5faf-4494-bd76-8bfa6b798ac5/download
File
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application/pdf
DiscoveringHIV_RelatedInformation_2022.pdf
URL
https://repisalud.isciii.es/bitstreams/3258426c-8f1f-4a15-9ecd-af0baa8f7088/download
File
MD5
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text/plain
DiscoveringHIV_RelatedInformation_2022.pdf.txt