An Ontology-based Mediator of Clinical Information for Decision Support Systems

Y. Kawazoe; K. Ohe

doi:10.3414/ME9126

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2008; 47(06): 549-559
DOI: 10.3414/ME9126

Original Article

Schattauer GmbH

An Ontology-based Mediator of Clinical Information for Decision Support Systems

A Prototype of a Clinical Alert System for Prescriptions

Y. Kawazoe

¹Department of Planning, Information and Management, University of Tokyo Hospital, Tokyo, Japan

,

K. Ohe

²Department of Medical Informatics and Economics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

› Institutsangaben

Abstract

Summary

Objective: We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system.

Methods: Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period.

Results: The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction.

Conclusion: Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.

Keywords

Ontology - mediator - decision support system - Web Ontology Language

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Referenzen

References
1 Feigenbaum EA. The art of artificial intelligence: Themes and case studies of knowledge engineering. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence 1977. pp 1014-1029.
2 Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res 1975; 8 (04) 303-320.
3 Motta E. Reusable Components for Knowledge Modelling: Case Studies in Parametric Design Problem Solving. IOS Press; 1999. pp 1-12.
4 Wiederhold G, Fries J, Wey S. Structured organization of clinical databases. In: Proceedings of the American Federation of Information Processing Societies National Computer Conference 1975 pp 479-485.
5 Augusto JC. Temporal reasoning for decision support in medicine. Artif Intell Med 2005; 33 (01) 1-24.
6 Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member Submission May 21, 2004 (cited June 1, 2008). Available from: http://www.w3.org/Submission/SWRL/
7 Wiederhold G. Mediators in the architecture of future systems. Computer 1992; 25 (03) 38-49.
8 Mrissa M, Ghedira C, Benslimane D, Maamar Z, Rosenberg F, Dustdar S. A context-based mediation approach to compose semantic Web services. ACM Transactions on Internet Technology 2007 8 (1): Article No. 4.
9 Bechhofer S, Harmelen FV, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA. OWL Web Ontology Language Reference. W3C Recommendation February 10, 2004 (cited June 1, 2008). Available from: http://www.w3.org/TR/owl-ref/
10 Musen MA, Gennari JH, Eriksson H, Tu SW, Puerta AR. PROTEGE-II: a suite of tools for development of intelligent systems from reusable components. In: Proc Annu Symp Comput Appl Med Care. 1994 p 1065.
11 Knublauch H, Fergerson RW, Noy NF, Musen MA. The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications. In Proceedings of the 3rd International Semantic Web Conference (ISWC) 2004
12 O’Connor MJ, Knublauch H, Tu SW, Grosof B, Dean M, Grosso W, Musen MA. Supporting rule system interoperability on the semantic web with SWRL. In: Proceedings of the 4th International Semantic Web Conference (ISWC) 2005 pp 974-986.
13 Allen JF. Maintaining Knowledge about Temporal Intervals. Communications of the ACM 1983; 26 (11) 832-843.
14 Shahar Y. A framework for knowledge-based temporal abstraction. Artif Intell Med 1997; 90 1-2 79-133.
15 Chakravarty S, Shahar Y. Acquisition and analysis of repeating patterns in time-oriented clinical data. Methods Inf Med 2001; 40 (05) 410-420.
16 Seyfang A, Miksch S. Advanced temporal data abstraction for guideline execution. Stud Health Technol Inform 2004; 101: 88-102.
17 Analyti A, Antoniou G, Damasio CV, Wagner G. Negation and Negative Information in the W3C Resource Description Framework. Annals of Mathematics, Computing and Teleinformatics 2004; 1 (02) 25-34.
18 Massachusetts Institute of Technology.. The SIMILE Project (cited June 1, 2008). Available from: http://simile.mit.edu/
19 O’Connor MJ, Shankar RD, Das AK. An Ontology- Driven Mediator for Querying Time- Oriented Biomedical Data. In: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems 2006 pp 264-269.
20 Shankar RD, Martins SB, O’Connor MJ, Parrish DB, Das AK. Epoch: an ontological framework to support clinical trials management. Conference on Information and Knowledge Management. In: Proceedings of the international workshop on Healthcare information and knowledge management 2006. pp 25-32.
21 Doan A, Madhavan J, Domingos P, Halevy A. Learning to map between ontologies on the semantic web. In: Proceedings of the Eleventh International WWW Conference 2002
22 Mena E, Illarramendi A, Kashyap V, Sheth AP. OBSERVER: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies. Distributed and Parallel Databases 2000; 8 (02) 223-271.
23 Stumme G, Madche A. FCA-Merge: Bottom-up merging of ontologies. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence 2001. pp 225-230.
24 Takabayashi K, Ho TB, Yokoi H, Nguyen TD, Kawasaki S, Le SQ, Suzuki T, Yokosuka O. Temporal abstraction and data mining with visualization of laboratory data. Stud Health Technol Inform 2007; 129 Pt 2 1304-1308.