Evidence map›Paper›PMID 39631779›Full record

ArticleApplied clinical informatics2024

Evolution of a Graph Model for the OMOP Common Data Model.

Mengjia Kang, Jose A Alvarado-Guzman, Luke V Rasmussen, Justin B Starren

Abstract read
In one paragraph

Article in Applied clinical informatics, 2024. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mengjia KangDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.
Jose A Alvarado-GuzmanNeo4j, Inc., San Mateo, California, United States.
Luke V RasmussenDivision of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States.
Justin B StarrenDivision of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States.

Funding

Technology CoreU19AI135964 · NIAID · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LUIS A. Nunes AMARAL · 2018 to 2026
$24.7M
NIAID NIH HHS U19 AI135964
6 · The paper itself

Abstract

objectiveGraph databases for electronic health record (EHR) data have become a useful tool for clinical research in recent years, but there is a lack of published methods to transform relational databases to a graph database schema. We developed a graph model for the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that can be reused across research institutions.

methodsWe created and evaluated four models, representing two different strategies, for converting the standardized clinical and vocabulary tables of OMOP into a property graph model within the Neo4j graph database. Taking the Successful Clinical Response in Pneumonia Therapy (SCRIPT) and Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL) cohorts as test datasets with different sizes, we compared two of the resulting graph models with respect to database performance including database building time, query complexity, and runtime for both cohorts.

resultsUtilizing a graph schema that was optimized for storing critical information as topology rather than attributes resulted in a significant improvement in both data creation and querying. The graph database for our larger cohort, CRITICAL, can be built within 1 hour for 134,145 patients, with a total of 749,011,396 nodes and 1,703,560,910 edges. DISCUSSION: To our knowledge, this is the first generalized solution to convert the OMOP CDM to a graph-optimized schema. Despite being developed for studies at a single institution, the modeling method can be applied to other OMOP CDM v5.x databases. Our evaluation with the SCRIPT and CRITICAL cohorts and comparison between the current and previous versions show advantages in code simplicity, database building, and query speed.

conclusionWe developed a method for converting OMOP CDM databases into graph databases. Our experiments revealed that the final model outperformed the initial relational-to-graph transformation in both code simplicity and query efficiency, particularly for complex queries.

Indexed as

Databases, FactualElectronic Health RecordsComputer GraphicsHumans

Identifiers

PMID39631779
PMCPMC11617070

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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.