Automatic detection and resolution of measurement-unit conflicts in aggregated data.

Soroush Samadian,Mark D Wilkinson,Bruce Mcmanus

doi:10.1186/1755-8794-7-s1-s12

Soroush Samadian, Mark D Wilkinson + Show 1 more

Open Access

https://doi.org/10.1186/1755-8794-7-s1-s12

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Abstract

BackgroundMeasurement-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.MethodsWe 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.ResultsWe 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.ConclusionsWe demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.

Highlights

Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis
A SPARQL query is provided to the SHARE query client that searches for high blood pressure measurements, as follows: SELECT ?record ?convertedvalue ?riskgrade FROM WHERE
} In the above query SHARE examines the HighSystolicBloodPressure class and discovers the “sio:has unit” value om:kilopascal axiom, indicating that measurements of High Systolic Blood Pressure will need to be expressed in kilopascals

Summary

Introduction

Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. Integration, comparison and interpretation of quantitative data require, as a first step, that all measurements are represented in the same units. Even NASA has made serious and expensive errors by failing to detect and account for measurement-unit conflicts[1]. This problem is well-recognized in clinical research, due to its complex, multi-dimensional and heterogeneous nature, and where highly disparate datasets, often from non-coordinating groups, need to be brought together. The reader more interested in detailed core theoretical foundations on which this study (and many existing ontologies such as DOLCE) is based, is referred to (among others) [3,4,5]

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