J Korean Soc Med Inform.  2006 Sep;12(3):261-271.

How to Deal with Medical Narratives based on Controlled Natural Language Operated by an Ontology

Affiliations
  • 1Department of Industrial Engineering, College of Engineering, Pusan National University, Korea.
  • 2Department of Dental Care and Information Technology, School of Dentistry, Seoul National University, Korea.
  • 3Department of Oral Pathology, School of Dentistry, Seoul National University, Korea.
  • 4Department of Dental Management & Informatics, School of Dentistry, Seoul National University, Korea. meeree@snu.ac.kr

Abstract


OBJECTIVE
Medical narratives entry is a major issue to be solved in developing an electronic medical record system operating in practice, as they are, in large part, described in a free-text format. The issue can be dealt with in three aspects: to improve the reusability by structuring medical narratives, to support clinical pragmatics in medical fields, and to reduce the burden of data entry. With the aspects having in mind, this paper purports to present an ontological method for better way of medical narratives entry.
METHODS
We developed an ontology for which medical knowledge is structurally represented. Then we can enter medical narrative texts with commands of the controlled natural language operable on the ontology model.
RESULTS
Many theoretical studies on free-text entry were reviewed, based on which an authoring and editing tool for natural language description operable on the ontology model has been developed and tested. The performance of the tool is satisfactory within the limit of the domain models we developed here.
CONCLUSION
The results of this paper are contributive for clinicians to make an easy entry of medical narratives as far as the ontology model covers their knowledge domain. It is also expected that the cost in recording medical narratives might be considerably reduced and data quality can be improved.

Keyword

EMR; Medical Narrative; Controlled Natural Language; Ontology; User Interface

MeSH Terms

Electronic Health Records
Models, Theoretical
Data Accuracy
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