Literature DB >> 29678046

Generalizing the Arden Syntax to a Common Clinical Application Language.

Stefan Kraus1.   

Abstract

The Arden Syntax for Medical Logic Systems is a standard for encoding and sharing knowledge in the form of Medical Logic Modules (MLMs). Although the Arden Syntax has been designed to meet the requirements of data-driven clinical event monitoring, multiple studies suggest that its language constructs may be suitable for use outside the intended application area and even as a common clinical application language. Such a broader context, however, requires to reconsider some language features. The purpose of this paper is to outline the related modifications on the basis of a generalized Arden Syntax version. The implemented prototype provides multiple adjustments to the standard, such as an option to use programming language constructs without the frame-like MLM structure, a JSON compliant data type system, a means to use MLMs as user-defined functions, and native support of restful web services with integrated data mapping. This study does not aim to promote an actually new language, but a more generic version of the proven Arden Syntax standard. Such an easy-to-understand domain-specific language for common clinical applications might cover multiple additional medical subdomains and serve as a lingua franca for arbitrary clinical algorithms, therefore avoiding a patchwork of multiple all-purpose languages between, and even within, institutions.

Entities:  

Keywords:  Arden Syntax; Domain-specific language; Medical Logic Modules

Mesh:

Year:  2018        PMID: 29678046

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  2 in total

1.  Mapping the Entire Record-An Alternative Approach to Data Access from Medical Logic Modules.

Authors:  Stefan Kraus; Dennis Toddenroth; Martin Staudigel; Wolfgang Rödle; Philipp Unberath; Lena Griebel; Hans-Ulrich Prokosch; Sebastian Mate
Journal:  Appl Clin Inform       Date:  2020-05-13       Impact factor: 2.342

2.  Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model.

Authors:  Christian Maier; Lorenz A Kapsner; Sebastian Mate; Hans-Ulrich Prokosch; Stefan Kraus
Journal:  Appl Clin Inform       Date:  2021-01-27       Impact factor: 2.342

  2 in total

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