Units of Measure in Clinical Information Systems

Schadow, Gunther; McDonald, Clement J.; Suico, Jeffrey G.; Föhring, U; Tolxdorff, Thomas

doi:10.1136/jamia.1999.0060151

Cited by 43 publications

(28 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, that might be a cumbersome task initially, which can be avoided to some extend using a subset from ISO 21090. Commonalties and differences of the data types in clinical modeling are listed in Table 2, including the use of units such as Unified Code for Units of Measure (UCUM) [34]. …”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Detailed Clinical Models: A Review

2010

View full text Add to dashboard Cite

ObjectivesDue to the increasing use of electronic patient records and other health care information technology, we see an increase in requests to utilize these data. A highly level of standardization is required during the gathering of these data in the clinical context in order to use it for analyses. Detailed Clinical Models (DCM) have been created toward this purpose and several initiatives have been implemented in various parts of the world to create standardized models. This paper presents a review of DCM.MethodsTwo types of analyses are presented; one comparing DCM against health care information architectures and a second bottom up approach from concept analysis to representation. In addition core parts of the draft ISO standard 13972 on DCM are used such as clinician involvement, data element specification, modeling, meta information, and repository and governance.ResultsSix initiatives were selected: Intermountain Healthcare, 13606/OpenEHR Archetypes, Clinical Templates, Clinical Contents Models, Health Level 7 templates, and Dutch Detailed Clinical Models. Each model selected was reviewed for their overall development, involvement of clinicians, use of data types, code bindings, expressing semantics, modeling, meta information, use of repository and governance.ConclusionsUsing both a top down and bottom up approach to comparison reveals many commonalties and differences between initiatives. Important differences include the use of or lack of a reference model and expressiveness of models. Applying clinical data element standards facilitates the use of conceptual DCM models in different technical representations.

show abstract

Section: Resultsmentioning

confidence: 99%

“…1) Data types: that is the use of different types of data such as free text, (coded) value sets and physical quantities (ISO 21090), including units of measurements (Schadow and McDonald [34]).…”

Section: Methodsmentioning

confidence: 99%

Detailed Clinical Models: A Review

2010

View full text Add to dashboard Cite

show abstract

“…as EngMath (Gruber & Olsen, 1994), Measurement units in clinical information systems, UCUM (Schadow et al, 1999), Quantities, Units, Dimensions and Data Types Ontologies, QUDT (Hodgson et al, 2013), units.obo (Gkoutos, 2011) or Ontology of Measurement units and Related Concepts, OM (Rijgersberg et al, 2013) in order to exchange and process quantitative information. However, domain authors can freely use typographic variations to write measurement units in scientific documents.…”

Section: Several Domain Ontologies Have Been Modeled For Units and Mementioning

confidence: 99%

Xart: Discovery of correlated arguments of n-ary relations in text

Berrahou

Buche

Dibie-Barthelemy

et al. 2017

Expert Systems with Applications

View full text Add to dashboard Cite

Here we present the Xart system based on a three-step hybrid method using data mining approaches and syntactic analysis to automatically discover and extract relevant data modeled as n-ary relations in plain text. A n-ary relation links a studied object with its features considered as several arguments. We addressed the challenge of designing a novel method to handle the identification and extraction of heterogeneous arguments such as symbolic arguments, quantitative arguments composed of numbers and various measurement units. We thus developed the Xart system, which relies on a domain ontology for discovering patterns, in plain text, to identify arguments involved in n-ary relations. The discovered patterns take advantage of different ontological levels that facilitate identification of all arguments and pool them in the sought n-ary relation. (Résumé d'auteur

show abstract

“…Nevertheless, the choice of units, even within this system, is sufficiently broad that reliable automated integration of quantitative measurements remains problematic [7], and as a result, a consistent methodology to interpret and integrate the units within and between datasets remains to be established.…”

Section: Related Workmentioning

confidence: 99%

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

Samadian¹,

McManus²,

Wilkinson

2014

BMC Med Genomics

View full text Add to dashboard Cite

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.

show abstract

Units of Measure in Clinical Information Systems

Cited by 43 publications

References 9 publications

Detailed Clinical Models: A Review

Detailed Clinical Models: A Review

Xart: Discovery of correlated arguments of n-ary relations in text

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

Contact Info

Product

Resources

About