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Multimorbidity prediction using link prediction
Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360941/ https://www.ncbi.nlm.nih.gov/pubmed/34385524 http://dx.doi.org/10.1038/s41598-021-95802-0 |
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author | Aziz, Furqan Cardoso, Victor Roth Bravo-Merodio, Laura Russ, Dominic Pendleton, Samantha C. Williams, John A. Acharjee, Animesh Gkoutos, Georgios V. |
author_facet | Aziz, Furqan Cardoso, Victor Roth Bravo-Merodio, Laura Russ, Dominic Pendleton, Samantha C. Williams, John A. Acharjee, Animesh Gkoutos, Georgios V. |
author_sort | Aziz, Furqan |
collection | PubMed |
description | Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score. |
format | Online Article Text |
id | pubmed-8360941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83609412021-08-13 Multimorbidity prediction using link prediction Aziz, Furqan Cardoso, Victor Roth Bravo-Merodio, Laura Russ, Dominic Pendleton, Samantha C. Williams, John A. Acharjee, Animesh Gkoutos, Georgios V. Sci Rep Article Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score. Nature Publishing Group UK 2021-08-12 /pmc/articles/PMC8360941/ /pubmed/34385524 http://dx.doi.org/10.1038/s41598-021-95802-0 Text en © The Author(s) 2021 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 Aziz, Furqan Cardoso, Victor Roth Bravo-Merodio, Laura Russ, Dominic Pendleton, Samantha C. Williams, John A. Acharjee, Animesh Gkoutos, Georgios V. Multimorbidity prediction using link prediction |
title | Multimorbidity prediction using link prediction |
title_full | Multimorbidity prediction using link prediction |
title_fullStr | Multimorbidity prediction using link prediction |
title_full_unstemmed | Multimorbidity prediction using link prediction |
title_short | Multimorbidity prediction using link prediction |
title_sort | multimorbidity prediction using link prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360941/ https://www.ncbi.nlm.nih.gov/pubmed/34385524 http://dx.doi.org/10.1038/s41598-021-95802-0 |
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