title: Automatic detection and resolution of measurement-unit conflicts in aggregated data.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.
authors: Soroush
authors: S
authors: Samadian
authors: Bruce
authors: B
authors: McManus
authors: Mark
authors: M
authors: Wilkinson
mesh_terms: D001331:Automation; D064229:Biological Ontologies; D002318:Cardiovascular Diseases / Q000235:genetics; D006801:Humans; D020407:Internet; D008490:Medical Informatics / Q000379:methods*; D010641:Phenotype; D012660:Semantics