Results 11 to 20 of about 5,745 (195)
Extract, transform, load framework for the conversion of health databases to OMOP. [PDF]
PLoS ONE, 2022 Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model
Juan C Quiroz +5 more
doaj +4 more sources
Adaption of the OMOP CDM for Rare Diseases [PDF]
, 2021 The OMOP Common Data Model (OMOP CDM) is an option to store patient data and to use these in an international context. Up to now, rare diseases can only be partly described in OMOP CDM. Therefore, it is necessary to investigate which special features in the context of rare diseases (e.g.
Michele, Zoch +6 more
openaire +2 more sources
FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model
Journal of Biomedical Informatics, 2022 Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as
Guohui Xiao +10 more
openaire +4 more sources
The Usage of OHDSI OMOP – A Scoping Review [PDF]
, 2021 OHDSI, a fast growing open-science research community seeks to enable researchers from around the globe to conduct network studies based on standardized data and vocabularies. There is no comprehensive review of publications about OHDSI’s standard: the OMOP Common Data Model and its usage available.
Ines, Reinecke +4 more
openaire +2 more sources
JMIR Medical Informatics, 2021 BackgroundIn the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data.
Nicolas Paris +2 more
doaj +1 more source
Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea [PDF]
Healthcare Informatics Research, 2022 Objectives The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in ...
Hui Xing Tan +10 more
doaj +1 more source
Preserving Privacy when Querying OMOP CDM Databases
, 2022 Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid inadequate disclosure is to ensure that all data that can be associated directly with an ...
Joao Rafael, Almeida +2 more
openaire +2 more sources
How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias [PDF]
, 2020 Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing.
Brown, Jeffrey S. +5 more
core +2 more sources
Deep-learning-based automated terminology mapping in OMOP-CDM
Journal of the American Medical Informatics Association, 2021 Abstract Objective Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision ...
Byungkon Kang +5 more
openaire +2 more sources
EHR-Independent Predictive Decision Support Architecture Based on OMOP [PDF]
Applied Clinical Informatics, 2020 Abstract Background The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk.
Unberath, Philipp +5 more
openaire +2 more sources