Automatic detection and resolution of measurement-unit conflicts in aggregated data. Samadian, S., McManus, B., & Wilkinson, M. BMC Medical Genomics, 7(Suppl 1):S12, 2014. ![Automatic detection and resolution of measurement-unit conflicts in aggregated data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Motivation: Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. As multi-national collaborations grow, as new measurement instruments appear, and as Linked Open Data infrastructures become increasingly pervasive, the number of such conflicts will similarly increase. We propose a generic approach to the problem of (a) encoding measurement units in datasets in a machine-readable manner, (b) detecting when a dataset contained mixtures of measurement units, and (c) automatically converting any conflicting units into a desired unit, as defined for a given study. Results: We utilized existing ontologies and standards for scientific data representation, measurement unit definition, and data manipulation to build a simple and flexible Semantic Web Service-based approach to measurement-unit harmonization. A cardiovascular patient cohort in which clinical measurements were recorded in a number of different units (e.g., mmHg and cmHg for blood pressure) was automatically classified into a number of clinical phenotypes, semantically defined using different measurement units. Conclusion: We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.
@article{
title = {Automatic detection and resolution of measurement-unit conflicts in aggregated data},
type = {article},
year = {2014},
keywords = {Clinical Research,Measurement-units,Ontologies,Semantic Web Services},
pages = {S12},
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last_modified = {2017-03-22T07:45:59.566Z},
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abstract = {Motivation: Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. As multi-national collaborations grow, as new measurement instruments appear, and as Linked Open Data infrastructures become increasingly pervasive, the number of such conflicts will similarly increase. We propose a generic approach to the problem of (a) encoding measurement units in datasets in a machine-readable manner, (b) detecting when a dataset contained mixtures of measurement units, and (c) automatically converting any conflicting units into a desired unit, as defined for a given study. Results: We utilized existing ontologies and standards for scientific data representation, measurement unit definition, and data manipulation to build a simple and flexible Semantic Web Service-based approach to measurement-unit harmonization. A cardiovascular patient cohort in which clinical measurements were recorded in a number of different units (e.g., mmHg and cmHg for blood pressure) was automatically classified into a number of clinical phenotypes, semantically defined using different measurement units. Conclusion: We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.},
bibtype = {article},
author = {Samadian, Soroush and McManus, Bruce and Wilkinson, Mark},
doi = {10.1186/1755-8794-7-S1-S12},
journal = {BMC Medical Genomics},
number = {Suppl 1}
}
Downloads: 0
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