Heterogeneous graph construction and HinSAGE learning from electronic medical records
Abstract 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 aim...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25693-2 |
| _version_ | 1811203839450152960 |
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| author | Ha Na Cho Imjin Ahn Hansle Gwon Hee Jun Kang Yunha Kim Hyeram Seo Heejung Choi Minkyoung Kim Jiye Han Gaeun Kee Tae Joon Jun Young-Hak Kim |
| author_facet | Ha Na Cho Imjin Ahn Hansle Gwon Hee Jun Kang Yunha Kim Hyeram Seo Heejung Choi Minkyoung Kim Jiye Han Gaeun Kee Tae Joon Jun Young-Hak Kim |
| author_sort | Ha Na Cho |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T03:01:47Z |
| format | Article |
| id | doaj.art-699ffbd5d50a4b2090f6bb7074491330 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T03:01:47Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-699ffbd5d50a4b2090f6bb70744913302022-12-22T03:50:37ZengNature PortfolioScientific Reports2045-23222022-12-011211910.1038/s41598-022-25693-2Heterogeneous graph construction and HinSAGE learning from electronic medical recordsHa Na Cho0Imjin Ahn1Hansle Gwon2Hee Jun Kang3Yunha Kim4Hyeram Seo5Heejung Choi6Minkyoung Kim7Jiye Han8Gaeun Kee9Tae Joon Jun10Young-Hak Kim11Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineBig Data Research Center, Asan Institute for Life Sciences, Asan Medical CenterDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineAbstract 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.https://doi.org/10.1038/s41598-022-25693-2 |
| spellingShingle | Ha Na Cho Imjin Ahn Hansle Gwon Hee Jun Kang Yunha Kim Hyeram Seo Heejung Choi Minkyoung Kim Jiye Han Gaeun Kee Tae Joon Jun Young-Hak Kim Heterogeneous graph construction and HinSAGE learning from electronic medical records Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-25693-2 |
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