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Discovering HIV related information by means of association rules and machine learning
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616424/ https://www.ncbi.nlm.nih.gov/pubmed/36307506 http://dx.doi.org/10.1038/s41598-022-22695-y |
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author | Araujo, Lourdes Martinez-Romo, Juan Bisbal, Otilia Sanchez-de-Madariaga, Ricardo |
author_facet | Araujo, Lourdes Martinez-Romo, Juan Bisbal, Otilia Sanchez-de-Madariaga, Ricardo |
author_sort | Araujo, Lourdes |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9616424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96164242022-10-30 Discovering HIV related information by means of association rules and machine learning Araujo, Lourdes Martinez-Romo, Juan Bisbal, Otilia Sanchez-de-Madariaga, Ricardo Sci Rep Article 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. Nature Publishing Group UK 2022-10-28 /pmc/articles/PMC9616424/ /pubmed/36307506 http://dx.doi.org/10.1038/s41598-022-22695-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Araujo, Lourdes Martinez-Romo, Juan Bisbal, Otilia Sanchez-de-Madariaga, Ricardo Discovering HIV related information by means of association rules and machine learning |
title | Discovering HIV related information by means of association rules and machine learning |
title_full | Discovering HIV related information by means of association rules and machine learning |
title_fullStr | Discovering HIV related information by means of association rules and machine learning |
title_full_unstemmed | Discovering HIV related information by means of association rules and machine learning |
title_short | Discovering HIV related information by means of association rules and machine learning |
title_sort | discovering hiv related information by means of association rules and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616424/ https://www.ncbi.nlm.nih.gov/pubmed/36307506 http://dx.doi.org/10.1038/s41598-022-22695-y |
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