Evidence map›Paper›PMID 36477457›Full record

ArticleScientific reports2022

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

Ha Na Cho, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Hyeram Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee and 2 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Ha Na ChoDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Imjin AhnDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Hansle GwonDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Hee Jun KangDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Yunha KimDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Hyeram SeoDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Heejung ChoiDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Minkyoung KimDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Jiye HanDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Gaeun KeeDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Tae Joon Jun *Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea.
Young-Hak Kim *Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43 Gil, Songpagu, 05505, Seoul, Republic of Korea. mdyhkim@amc.seoul.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Electronic Health RecordsHumans

Identifiers

PMID36477457
PMCPMC9729175

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.