Literature DB >> 7889770

The GALEN project.

A L Rector1, W A Nowlan.   

Abstract

The GALEN project is developing language independent concept representation systems as the foundations for the next generation of multilingual coding systems. It aims to support the flexibility required to cope with the diversity amongst medical applications, while ensuring the coherence necessary for integration and re-use of terminologies. GALEN is developing a fully compositional and generative formal system for modelling concepts: the GALEN Representation and Integration Language (GRAIL) Kernel. Its goal is to overcome many of the problems with traditional coding and classification systems, in particular the combinatorial explosion of terms in enumerative systems and the generation of nonsensical terms in partially compositional systems. It will also provide a clean separation between the concept model and linguistic mechanisms which interpret that model (i.e., the words in a specific language, syntax, alternative phrasings, etc.) in order to allow the development of multilingual systems. GRAIL aims to be formally sound and produce models that are verifiable and contain no contradictions or ambiguities, with realistic human effort. A Coding Reference (CORE) Model of medical terminology covering is being developed which aims to represent the core concepts in for example pathology, anatomy and therapeutics, that have widespread applicability in medical applications. It should also provide the basis for specialist extensions according to the formal principles of GRAIL. The main results of GALEN will be delivered as a Terminology Server (TeS) which encapsulates and coordinates the functionality of the concept module, multilingual module, and code conversion module, and also provides a uniform applications programming interface and network services for use by external applications.

Entities:  

Mesh:

Year:  1994        PMID: 7889770     DOI: 10.1016/0169-2607(94)90020-5

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  19 in total

1.  Terminology Query Language: a server interface for concept-oriented terminology systems.

Authors:  M A Hogarth; M Gertz; F A Gorin
Journal:  Proc AMIA Symp       Date:  2000

2.  A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development.

Authors:  S L Achour; M Dojat; C Rieux; P Bierling; E Lepage
Journal:  J Am Med Inform Assoc       Date:  2001 Jul-Aug       Impact factor: 4.497

3.  SNOMED clinical terms: overview of the development process and project status.

Authors:  M Q Stearns; C Price; K A Spackman; A Y Wang
Journal:  Proc AMIA Symp       Date:  2001

4.  Ontological realism: A methodology for coordinated evolution of scientific ontologies.

Authors:  Barry Smith; Werner Ceusters
Journal:  Appl Ontol       Date:  2010-11-15       Impact factor: 1.115

5.  Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results.

Authors:  O Dameron; B Gibaud; X Morandi
Journal:  Surg Radiol Anat       Date:  2004-04-30       Impact factor: 1.246

6.  International classification of diseases, 10th edition, clinical modification and procedure coding system: descriptive overview of the next generation HIPAA code sets.

Authors:  Steven J Steindel
Journal:  J Am Med Inform Assoc       Date:  2010 May-Jun       Impact factor: 4.497

7.  An electronic health record based on structured narrative.

Authors:  Stephen B Johnson; Suzanne Bakken; Daniel Dine; Sookyung Hyun; Eneida Mendonça; Frances Morrison; Tiffani Bright; Tielman Van Vleck; Jesse Wrenn; Peter Stetson
Journal:  J Am Med Inform Assoc       Date:  2007-10-18       Impact factor: 4.497

8.  A randomized controlled trial of automated term composition.

Authors:  P L Elkin; K R Bailey; C G Chute
Journal:  Proc AMIA Symp       Date:  1998

9.  Development of the Logical Observation Identifier Names and Codes (LOINC) vocabulary.

Authors:  S M Huff; R A Rocha; C J McDonald; G J De Moor; T Fiers; W D Bidgood; A W Forrey; W G Francis; W R Tracy; D Leavelle; F Stalling; B Griffin; P Maloney; D Leland; L Charles; K Hutchins; J Baenziger
Journal:  J Am Med Inform Assoc       Date:  1998 May-Jun       Impact factor: 4.497

10.  Automatic knowledge acquisition from medical texts.

Authors:  U Hahn; K Schnattinger; M Romacker
Journal:  Proc AMIA Annu Fall Symp       Date:  1996
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