The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance. In this paper, we propose the implementation of a new reasoner based on categorical logic over a translation of OMOP-CDM into an ontology model. This enables enhancements to the efficiency and scalability of implementing such models.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/SHTI240680 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
OntoBridge Versus Traditional ETL: Enhancing Data Standardization into CDM Formats Using Ontologies Within the DATOS-CAT Project.
Stud Health Technol Inform
August 2024
Clinical Informatics Service, Hospital Clínic de Barcelona. 08036 - Barcelona, Spain.
Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM.
View Article and Find Full Text PDFEnhancing Healthcare Informatics: Integrating Category Theory Reasoning into OMOP-CDM Ontology Model.
Stud Health Technol Inform
August 2024
Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, UMR 1142, F-93000, Bobigny, France.
The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance.
View Article and Find Full Text PDFOHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.
J Am Med Inform Assoc
February 2024
Coordinating Center, Observational Health Data Sciences and Informatics, New York City NY 10032, United States.
Article Synopsis
- OHDSI (Observational Health Data Sciences and Informatics) is a massive distributed data network with over 331 sources and 2.1 billion patient records, facilitating large-scale observational research through standardized data.
- The OHDSI Standardized Vocabularies, a crucial component of this network, include more than 10 million concepts from 136 vocabularies, allowing for better data harmonization and easier research execution.
- This open-source vocabulary system addresses challenges in observational research, enabling various analyses such as efficient phenotyping and patient-level predictions, and encourages researchers to utilize and contribute to its ongoing development.
Evaluation of OMOP CDM, i2b2 and ICGC ARGO for supporting data harmonization in a breast cancer use case of a multicentric European AI project.
J Biomed Inform
November 2023
Oncology Service, Hospital Clínic de Barcelona, Villarroel 170, 08036 Barcelona, Spain.
Objective: Observational research in cancer poses great challenges regarding adequate data sharing and consolidation based on a homogeneous data semantic base. Common Data Models (CDMs) can help consolidate health data repositories from different institutions minimizing loss of meaning by organizing data into a standard structure. This study aims to evaluate the performance of the Observational Medical Outcomes Partnership (OMOP) CDM, Informatics for Integrating Biology & the Bedside (i2b2) and International Cancer Genome Consortium, Accelerating Research in Genomic Oncology (ICGC ARGO) for representing non-imaging data in a breast cancer use case of EuCanImage.
View Article and Find Full Text PDFTransforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research.
Clin Epidemiol
September 2023
Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Article Synopsis
- The study aimed to convert the SIDIAP data system in Catalonia, Spain, to the OMOP Common Data Model and analyze COVID-19 outcomes in the general population.
- Researchers mapped patient-level data and conducted over 3,400 quality checks, creating a cohort of individuals from March 2020 to June 2022 to assess COVID-19 diagnoses, hospitalizations, ICU admissions, deaths, and vaccinations.
- The transformed database included 5.9 million individuals, revealing insights about COVID-19 demographics and outcomes, making it a valuable resource for future research in the area.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!