Validation of a common data model for active safety surveillance research

By J. M. Overhage, Patrick B. Ryan, C. Reich, Abraham G Hartzema, Paul E. Stang. J. Am. Medical Informatics Assoc., 2012

504 citations recorded by the source corpus.

Subfields: Emergency Medical Services, Epidemiology, Toxicology

View Paper: https://www.semanticscholar.org/paper/5c4cb597fc6b9dfd866b5edd2af485a61eb2f8bf

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In Epidemiology

In Toxicology

George Hripcsak

Citations in the corpus

Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017, unknown): “ f P y A A C B O A ( ) ( ) log ( , ) (3) i i T i F i i : : (1) : 2 :”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, unknown): “Examples of such large-scale phenotyping efforts are typ-iﬁed by the Electronic Medical Records and Genomics (eMERGE) Network [34] and the Observational Medical Outcomes Partner-ship (OMOP) [35].”
Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers (2015, methodology): “OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP) [4], which was a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.”
The Human Phenotype Ontology in 2024: phenotypes around the world (2023, background): “Common Data Models (CDMs) such as the Observational Medical Outcomes Partnership (OMOP) ( 52 ), are instrumental in making clinical data useful for research.”
Feasibility and utility of applications of the common data model to multiple, disparate observational health databases (2015, unknown): “Have atrial fibrillation or flutter 5093 (21) 11 075 (44) 16 202 (16) 28 499 (42) 1822 (18)”
CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines (2017, methodology): “…parsing and relation extraction), improving GUIs by conducing formal usability testing, normalizing its outputs to common data models such as those of the Observational Medical Outcomes Partnership, 41 as well as expanding the library of NLP pipelines for different information extraction tasks.”
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies (2020, unknown): “OMOP-CDM [81]).”