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

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.

Linked Open Data in Health and Clinical Care A Review of the Literature

“…As reported in Table 1, and in Figure 1, the literature search identified 37 citations in PubMed and 2806 citations in Scopus applying ¡"Linked Open Data"[All Fields]¿ as a search string. Specifying the search strings according to the aim of the review, and the inclusion criteria ( Figure 1), 9 papers have been considered and examined [23][24][25][26][27][28][29][30][31].…”

“…In one case, for paper 1 [23], three-star-linked open data requirements are met, as the developed Domesday dataset is available as Excel file and CSV format, ( Table 3, "Accessibility (availability), and maintenance" attribute). Finally, paper 4 [26], does not meet linked open data requirements as a tool with just exemplary data is available ( Table 3, "Accessibility (availability), and maintenance" attribute). As for patient data protection, it appears that it was properly addressed in 4 out of 9 papers (papers 1, 3, 7, and 8) [23,25,29,30], while it appears not be faced in 5 out of 9 papers (papers 2,4,5,6, and 9) [24,26,27,31].…”

“…In Table 4, the attributes for answering research question 3 are presented for each paper. In 5 out of 9 papers (papers 2,3,4,5, and 7) [24][25][26][27]29] the main contributions are strategies and methodologies for the integration of systems, including bridging the information gap among forms for clinical research and the one for patient care (Table 4, "Main Contributions" attribute). Then, in 3 out of 9 papers (papers 6,8, and 9) [28,30,31] the main contributions are the development of consistent triple stores ( Table 4, "Main Contributions" attribute).…”

“…The results of this review revealed the main significant applications in the field developed so far [23][24][25][26][27][28][29][30][31]. In addition, the papers analysed have been evaluated according to the 5 star-based scales for linked open data requirements [22].…”

Nanopublishing Clinical Diagnoses: Tracking Diagnostic Knowledge Base Content and Utilization

“…These restrictions might also include details such as levels of intensity, or value-ranges. For this purpose several ontologies such as Semanticscience Integrated Ontology (SIO) 3 , Measurement Unit Ontology (MUO) 4 , Units Ontology (UO) 5 or Quantities, Units, Dimensions and Data Types Ontologies (QUDT) 6 could be used to create these OWL restrictions, as was done in our previous work on encoding diagnostic rules in OWL [29], [34].…”