Literature DB >> 9082125

Issues in the structuring and acquisition of an ontology for medical language understanding.

P Zweigenbaum1, B Bachimont, J Bouaud, J Charlet, J F Boisvieux.   

Abstract

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

Mesh:

Year:  1995        PMID: 9082125

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  17 in total

1.  Issues in the design of medical ontologies used for knowledge sharing.

Authors:  A Burgun; G Botti; M Fieschi; P Le Beux
Journal:  J Med Syst       Date:  2001-04       Impact factor: 4.460

2.  Aggregating UMLS semantic types for reducing conceptual complexity.

Authors:  A T McCray; A Burgun; O Bodenreider
Journal:  Stud Health Technol Inform       Date:  2001

3.  A semantic lexicon for medical language processing.

Authors:  S B Johnson
Journal:  J Am Med Inform Assoc       Date:  1999 May-Jun       Impact factor: 4.497

4.  Building and evaluation of a structured representation of pharmacokinetics information presented in SPCs: from existing conceptual views of pharmacokinetics associated with natural language processing to object-oriented design.

Authors:  Catherine Duclos-Cartolano; Alain Venot
Journal:  J Am Med Inform Assoc       Date:  2003-01-28       Impact factor: 4.497

Review 5.  Computational approaches to phenotyping: high-throughput phenomics.

Authors:  Yves A Lussier; Yang Liu
Journal:  Proc Am Thorac Soc       Date:  2007-01

Review 6.  Desiderata for controlled medical vocabularies in the twenty-first century.

Authors:  J J Cimino
Journal:  Methods Inf Med       Date:  1998-11       Impact factor: 2.176

7.  Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base.

Authors:  C Rosse; J L Mejino; B R Modayur; R Jakobovits; K P Hinshaw; J F Brinkley
Journal:  J Am Med Inform Assoc       Date:  1998 Jan-Feb       Impact factor: 4.497

8.  Evaluating a normalized conceptual representation produced from natural language patient discharge summaries.

Authors:  P Zweigenbaum; J Bouaud; B Bachimont; J Charlet; J F Boisvieux
Journal:  Proc AMIA Annu Fall Symp       Date:  1997

9.  Symbolic anatomic knowledge representation in the Read Codes version 3: structure and application.

Authors:  E B Schulz; C Price; P J Brown
Journal:  J Am Med Inform Assoc       Date:  1997 Jan-Feb       Impact factor: 4.497

10.  An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

Authors:  Guo-Qiang Zhang; Guangming Xing; Licong Cui
Journal:  J Biomed Inform       Date:  2018-03-13       Impact factor: 6.317

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