Graph databases for openEHR clinical repositories

Helou, Samar; Kobayashi, Shinji; Yamamoto, Go; Kume, Naoto; Kondoh, Eiji; Hiragi, Shusuke; Okamoto, Kazuya; Tamura, Hiroshi; Kuroda, Tomohiro

doi:10.1504/ijcse.2019.103955

Cited by 9 publications

(5 citation statements)

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“…Contrastingly, graph databases link the data records to organize data features more effectively by focusing more in between the data points providing emphasis on the relationships 5,6 . In these, entities and links are used to increase the space e ciency and provide a faster querying period for large datasets compared to relational mapping 7 . There are three major advantages in applying graph databases: Firstly, a graph allows the total visualization of the full picture, which delivers simpli ed or alternative perspectives to otherwise complex problems.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Cho

Ahn

Gwon

et al. 2022

Preprint

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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 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 patient’s prognosis using 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 which provides analytical insights using 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 successfully predicted as baseline models. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular diseases event outcomes on supervised link prediction learning.

show abstract

Section: Introductionmentioning

confidence: 99%

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Cho

Ahn

Gwon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Contrastingly, graph databases link the data records to organize data features more effectively by focusing more on the data points providing emphasis on the relationships 5 , 6 . In these, entities and links are used to increase space efficiency and provide a faster querying period for large datasets compared to relational mapping 7 . There are three major advantages to applying graph databases: Firstly, a graph allows the total visualization of the full picture, which delivers simplified or alternative perspectives to otherwise complex problems.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Cho

Ahn

Gwon

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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.

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“…This feature lays a solid foundation for the development of a CDR. Some studies on openEHR-based CDRs have been proposed [ 18 - 20 ]. Second, openEHR divides models into the archetype model (AM) and the reference model (RM).…”

Section: Introductionmentioning

confidence: 99%

A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study

Cai

Nan

et al. 2021

JMIR Med Inform

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Background The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria. Objective The study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance. Methods A patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research. Results In total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67%). Conclusions We developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers.

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Graph databases for openEHR clinical repositories

Cited by 9 publications

References 0 publications

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Heterogeneous graph construction and HinSAGE learning from electronic medical records

A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study

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