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Discovering HIV related information by means of association rules and machine learning.

Authors :
Araujo, Lourdes
Martinez-Romo, Juan
Bisbal, Otilia
Sanchez-de-Madariaga, Ricardo
The Cohort of the National AIDS Network (CoRIS)
Portilla, Joaquín
Portilla, Irene
Merino, Esperanza
García, Gema
Agea, Iván
Sánchez-Payá, José
Rodríguez, Juan Carlos
Giner, Livia
Reus, Sergio
Boix, Vicente
Torrus, Diego
Pérez, Verónica
Portilla, Julia
Gómez, Juan Luís
Hernández, Jehovana
Source :
Scientific Reports; 10/28/2022, Vol. 12 Issue 1, p1-12, 12p
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
AIDS
MACHINE learning
HIV

Details

Language :
English
ISSN :
20452322
Volume :
12
Issue :
1
Database :
Complementary Index
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
159897949
Full Text :
https://doi.org/10.1038/s41598-022-22695-y