Building a Controlled Health Vocabulary in Japanese

Summary

Objectives: This study is aimed at developing a controlled clinical vocabulary for use in electronic patient record (EPR) systems.

Methods: In this paper, we propose a model for building the vocabulary. The model is composed of a Canonical Term Dictionary, an Atom Dictionary, a Composite Atom Dictionary, and an Index. Parsing and composing functions are included in this model. Canonical terms were extracted from reference terminologies. Atoms were extracted from the Canonical Term Dictionary and reduced to a set from which the Composite Atom Dictionary can be built. The index was built to link these two dictionaries. For testing the model, we compiled a sample vocabulary and applied the model to a SNOMED translation system (English to Japanese) and a term similarity estimation system.

Results: The sample vocabulary consisted of 15,600 atomic terms and 4,450 composite terms. 33,441 SNOMED terms were translated by the SNOMED translation system. The system gave adequate Japanese candidates in 56.3% of cases. The similarity estimation system found an average of 5.4 candidates when the equality ratio was over 50%.

Conclusions: The trial applications produced good results. The model seems promising for building a standard clinical vocabulary system. This system can be applied in certain other Asian countries, such as China and Korea.

Keywords

Controlled Vocabulary - Medical Dictionary - Terminology - Translations - Standards

• References

• 1 Hammond WE. Call for a Standard Clinical Vocabulary. JAMIA 1997; 4: 254-5.
• 2 O’Neil MJ, Payne C, Read JD. Read Codes Version 3: A User Led Terminology. Methods of Information in Medicine 1995; 34: 187-92.
• 3 National library of medicine.. Unified Medical Language System 11^th edition. NLM; 2000
• 4 Cote RA, Rothwell DJ, Palotay JL, Beckett RS, Brochu L. eds. The Systematized Nomenclature of Human and Veterinary Medicine: SNOMED International. College of American Pathologists. 1993
• 5 Satomura Y, Liu YB. et al. A structured Japanese medical Terminology aiming at CPR system. AMIA WG6 conference Washington DC.: 1999
• 6 American Society for Testing and Materials.. Standard Guide for Construction of a Clinical Nomenclature for Support of Electronic Health Records. ASTM E-1284-97.
• 7 Campbell JR, Carpenter P. et al. Phase II Evaluation of Clinical Coding Schemes: Completeness, Taxonomy, Mapping, Definitions, and Clarity. JAMIA 1997; 4: 238-51.
• 8 Oshima M. Medical Dictionary. Tokyo: Nichigai Associates Inc; 1989
• 9 Japanese Association of Medical Sciences Committee of Medical Terminology.. Japan Medical Terminology. Tokyo: Nanzando Company Limited; 1994
• 10 Department of MHW Statistical Information.. According to the ICD-10 Classification of Mental and Behavioural Disorders. MHW Statistical Association; 1995
• 11 Liu YB, Satomura Y. et al. Development of Structured Medical Terminology. The 19^th Joint Conference on Medial Informatics. Yokohama 1999: 760-1.
• 12 Satomura Y, Sasaki T. Standard Disease Name Terminology according to ICD-10. Japan Medical Journal 1998; 3876: 23-7.
• 13 Elkin PL, Tuttle M, Keck K, Campbell K, Atkin G, Chute CG. The Role of Compositionality in Standardized Problem List Generation. MEDINFO 98 660-4.
• 14 Satomura Y, Yamazaki S, Sasaki T. According to ICD10 Disease Name Terminology and Auto-coding System. The 15^th Joint Conference on Medial Informatics. 1995: 957-60.
• 15 Liu YB, Satomura Y. Development of the Editing Tools of Medical Terminology. The 18^th Joint Conference on Medial Informatics. Kobe, 1998: 372-3.
• 16 Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell D. Toward a Medical-concept Representation Language. JAMIA 1994; 1: 207-7.
• 17 Rassinoux AM, Miller RA, Baud RH, Scherrer JR. Modeling Concepts in Medicine for Medical Language Understanding. Methods of Information in Medicine 1998; 37: 361-72.
• 18 Johnson SB. Conceptual Graph Grammar- A Simple Formalism for Sublanguage. Methods of Information in Medicine 1998; 37: 345-52.
• 19 Sowa JP. Conceptual Graphs: information processing in mind and machine. Reading MA: Addison Wesley; 1984
• 20 Rassinoux AM, Miller RA, Baud RH, Scherrer JR. Modeling Concepts in Medicine for Medical Language Understanding. Methods of Information in Medicine 1998; 37: 361-72.
• 21 MacGregor R. The evolving technology of classification-based knowledge representation systems. Principles of Semantic Networks: Explorations in the Representation of Knowledge. San Mateo, CA: Morgan Kaufmann; 1991: 385-400.
• 22 Cimino JJ, Clayton PD, Hripcsak G, Johnson SB. Knowledge-based approaches to the maintenance of a Large Controlled Medical Terminology. JAMIA 1994; 1: 35-50.
• 23 Campbell KE, Das AK, Musen MA. A Logical Foundation for Clinical Data. JAMIA 1994; 1: 218-32.
• 24 Scherrer JR. Concepts, Knowledge and Language in Healthercare Information Systems: Follow-up 30 Months Later. Methods of Information in Medicine 1998; 37: 312-4.
• 25 Wiederhold G. Objects and Domains for Managing Medical Data and Knowledge. Method Inform Med 1995; 34: 40-6.
• 26 Cimino JJ. Desiderata for Controlled Medical Vocabularies in the Twenty-First Century. Method Inform Med 1998; 37: 394-403.
• 27 Amaral MB, Satomura Y. Processing Natural Language at Chiba University Hospital. M Computing Silver Springs, 1993; 1 (Suppl. 04) 6-15.
• 28 SNOMED CT,. http://www.snomed.org/sno medct_txt.html.