Methods Inf Med 2002; 41(01): 44-50
DOI: 10.1055/s-0038-1634312
Original Article
Schattauer GmbH

Theory, Abstraction and Design in Medical Informatics

V. Maojo
1   Artificial Intelligence Lab., Polytechnical University of Madrid, Campus de Montegancedo, Madrid
,
F. Martín
2   Bioinformatics Unit, Institute of Health Carlos III, Majadahonda, Madrid, Spain
,
J. Crespo
1   Artificial Intelligence Lab., Polytechnical University of Madrid, Campus de Montegancedo, Madrid
,
H. Billhardt
1   Artificial Intelligence Lab., Polytechnical University of Madrid, Campus de Montegancedo, Madrid
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

Summary

Objective: To analyze the scientific and engineering components of Medical Informatics. A clear characterization of these components should be undertaken to categorize different areas of Medical Informatics and create a research agenda for the future. Methods: We have adapted a classical ACM and IEEE report on computing to analyze Medical Informatics from three different viewpoints: Theory, Abstraction, and Design.

Results: We suggest that Medical Informatics can be considered from these three perspectives: (1) Theory, from which medical informaticians formally characterize the properties of the objects of study, creating new theories or using and adapting existing theories (e.g., from mathematics), (2) Abstraction, from which medical informaticians deal with all aspects of medical information and create new abstractions, methods, and technology-independent models, which can be experimentally verified, and (3) Design, from which medical informaticians develop systems or act as information brokers or advisors between medical and technology professionals, to improve the quality of computer applications in medicine.

Conclusion: Based on this framework, we suggest that Medical Informatics has an independent scientific character, different from other applied informatics areas. Finally, we analyze these three perspectives using data mining in medicine.

 
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