Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort's area of focus. Guided by work we have undertaken in developing the 'EMIF Knowledge Object Library', a semantic harmonization framework underpinning the collation of pan-European Alzheimer's cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks.
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JMIR Med Inform
January 2025
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States.
Background: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multicohort studies. However, given differences in variable encoding, accurate variable harmonization is difficult.
View Article and Find Full Text PDFFront Nutr
January 2025
Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia.
Introduction: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.
Methods: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge.
J Neurotrauma
January 2025
Division of Neuroscience, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science.
View Article and Find Full Text PDFEur Psychiatry
January 2025
Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Baden-Württemberg, Germany.
Background: Recent advances in natural language processing (NLP), particularly in language processing methods, have opened new avenues in semantic data analysis. A promising application of NLP is data harmonization in questionnaire-based cohort studies, where it can be used as an additional method, specifically when only different instruments are available for one construct as well as for the evaluation of potentially new construct-constellations. The present article therefore explores embedding models' potential to detect opportunities for semantic harmonization.
View Article and Find Full Text PDFData Brief
June 2024
Joint Research Center, European Commission, Ispra, Italy.
Urban focused semantically segmented datasets (e.g. ADE20k or CoCo) have been crucial in boosting research and applications in urban areas by providing rich sources of delineated objects in Street View Images (SVI).
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