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Objectives: To contrast the coverage of diseases between the Disease Ontology (DO) and SNOMED CT, and to compare the hierarchical structure of the two ontologies.
Methods: We establish a reference list of mappings. We characterize unmapped concepts in DO semantically and structurally. Finally, we compare the hierarchical structure between the two ontologies.
Results: Overall, 4478 (65%) the 6931 DO concepts are mapped to SNOMED CT. The cancer and neoplasm subtrees of DO account for many of the unmapped concepts. The most frequent differentiae in unmapped concepts include morphology (for cancers and neoplasms), specific subtypes (for rare genetic disorders), and anatomical subtypes. Unmapped concepts usually form subtrees, and less often correspond to isolated leaves or intermediary concepts.
Conclusion: This detailed analysis of the gaps in coverage and structural differences between DO and SNOMED CT contributes to the interoperability between these two ontologies and will guide further validation of the mapping.
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JMIR Med Inform
July 2025
Department of Emergency Medicine, The University of British Columbia, Vancouver, BC, Canada.
Background: Adverse drug events (ADEs) lead to more than 2 million emergency department visits in Canada annually, resulting in significant patient harm and more than CAD $1 billion in health care costs (in 2018, the average exchange rate for 1 CAD was 0.7711 USD; 1 billion CAD would have been approximately 771.1 million USD).
View Article and Find Full Text PDFPLoS One
January 2025
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Bremen, Germany.
Objective: The German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.
View Article and Find Full Text PDFAppl Clin Inform
October 2024
Department of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, Utah, United States.
Background: High-value care aims to enhance meaningful patient outcomes while reducing costs and is accelerated by curating data across health care systems through common data models (CDMs), such as Observational Medical Outcomes Partnership (OMOP). Meaningful patient outcomes, such as physical function, must be included in these CDMs. However, the extent to which physical therapy assessments are covered in the OMOP CDM is unclear.
View Article and Find Full Text PDFPLoS One
April 2024
Innovation and Research Centre for Multimorbidity, Slagelse Hospital, Slagelse, Denmark.
Multimorbidity, the presence of 2 or more chronic conditions in a person at the same time, is an increasing public health concern, which affects individuals through reduced health related quality of life, and society through increased need for healthcare services. Yet the structure of chronic conditions in individuals with multimorbidity, viewed as a population, is largely unmapped. We use algorithmic diagnoses and the K-means algorithm to cluster the entire 2015 Danish multimorbidity population into 5 clusters.
View Article and Find Full Text PDFJ Am Med Inform Assoc
November 2023
Department of Biomedical Informatics, Columbia University, New York City, NY 10032, United States.