Cargando…

A weighted patient network-based framework for predicting chronic diseases using graph neural networks

Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic diseas...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Haohui, Uddin, Shahadat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604920/
https://www.ncbi.nlm.nih.gov/pubmed/34799627
http://dx.doi.org/10.1038/s41598-021-01964-2
_version_ 1784602062686781440
author Lu, Haohui
Uddin, Shahadat
author_facet Lu, Haohui
Uddin, Shahadat
author_sort Lu, Haohui
collection PubMed
description Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.
format Online
Article
Text
id pubmed-8604920
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-86049202021-11-22 A weighted patient network-based framework for predicting chronic diseases using graph neural networks Lu, Haohui Uddin, Shahadat Sci Rep Article Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions. Nature Publishing Group UK 2021-11-19 /pmc/articles/PMC8604920/ /pubmed/34799627 http://dx.doi.org/10.1038/s41598-021-01964-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Lu, Haohui
Uddin, Shahadat
A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title_full A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title_fullStr A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title_full_unstemmed A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title_short A weighted patient network-based framework for predicting chronic diseases using graph neural networks
title_sort weighted patient network-based framework for predicting chronic diseases using graph neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604920/
https://www.ncbi.nlm.nih.gov/pubmed/34799627
http://dx.doi.org/10.1038/s41598-021-01964-2
work_keys_str_mv AT luhaohui aweightedpatientnetworkbasedframeworkforpredictingchronicdiseasesusinggraphneuralnetworks
AT uddinshahadat aweightedpatientnetworkbasedframeworkforpredictingchronicdiseasesusinggraphneuralnetworks
AT luhaohui weightedpatientnetworkbasedframeworkforpredictingchronicdiseasesusinggraphneuralnetworks
AT uddinshahadat weightedpatientnetworkbasedframeworkforpredictingchronicdiseasesusinggraphneuralnetworks