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Heterogeneous graph construction and HinSAGE learning from electronic medical records
Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to addr...
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/PMC9729175/ https://www.ncbi.nlm.nih.gov/pubmed/36477457 http://dx.doi.org/10.1038/s41598-022-25693-2 |
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author | Cho, Ha Na Ahn, Imjin Gwon, Hansle Kang, Hee Jun Kim, Yunha Seo, Hyeram Choi, Heejung Kim, Minkyoung Han, Jiye Kee, Gaeun Jun, Tae Joon Kim, Young-Hak |
author_facet | Cho, Ha Na Ahn, Imjin Gwon, Hansle Kang, Hee Jun Kim, Yunha Seo, Hyeram Choi, Heejung Kim, Minkyoung Han, Jiye Kee, Gaeun Jun, Tae Joon Kim, Young-Hak |
author_sort | Cho, Ha Na |
collection | PubMed |
description | Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient’s prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning. |
format | Online Article Text |
id | pubmed-9729175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97291752022-12-09 Heterogeneous graph construction and HinSAGE learning from electronic medical records Cho, Ha Na Ahn, Imjin Gwon, Hansle Kang, Hee Jun Kim, Yunha Seo, Hyeram Choi, Heejung Kim, Minkyoung Han, Jiye Kee, Gaeun Jun, Tae Joon Kim, Young-Hak Sci Rep Article Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient’s prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning. Nature Publishing Group UK 2022-12-07 /pmc/articles/PMC9729175/ /pubmed/36477457 http://dx.doi.org/10.1038/s41598-022-25693-2 Text en © The Author(s) 2022 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 Cho, Ha Na Ahn, Imjin Gwon, Hansle Kang, Hee Jun Kim, Yunha Seo, Hyeram Choi, Heejung Kim, Minkyoung Han, Jiye Kee, Gaeun Jun, Tae Joon Kim, Young-Hak Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title | Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title_full | Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title_fullStr | Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title_full_unstemmed | Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title_short | Heterogeneous graph construction and HinSAGE learning from electronic medical records |
title_sort | heterogeneous graph construction and hinsage learning from electronic medical records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729175/ https://www.ncbi.nlm.nih.gov/pubmed/36477457 http://dx.doi.org/10.1038/s41598-022-25693-2 |
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