Methods Inf Med 2005; 44(02): 262-264
DOI: 10.1055/s-0038-1633959
Original Article
Schattauer GmbH

Knowledge Logistics as an Intelligent Service for Healthcare

A. Smirnov
1   St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
,
M. Pashkin
1   St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
,
N. Chilov
1   St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
,
T. Levashova
1   St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
,
A. Krizhanovsky
1   St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
› Author Affiliations
Further Information

Publication History

Publication Date:
05 February 2018 (online)

Summary

Objectives: The technology of grid services is developing fast. This paper presents an approach to the implementation of an intelligent grid service that configures a hospital taking advantage of the knowledge logistics idea.

Methods: The presented approach is based on synergistic integration of knowledge acquired from distributed sources in order to obtain new or complement insufficient knowledge. Presented approach uses ontologies and the formalism of object-oriented constraint networks for knowledge representation and applies ILOG to constraint-based problem solving.

Results: The application of the approach is illustrated via a decision support system for a fictitious case study of a hospital configuration in the Binni region. The system showed the ability to produce interrelated solutions for subtasks of the hospital configuration task based on the built ontology and input parameters. Besides, such preferences as cost or time minimization are also considered by the implemented fuzzy logic module that finds a feasible solution.

Conclusions: KL would benefit from the grid, and particularly from the concept of a semantic grid. The scalable architecture of the approach enables its extension with regard to knowledge/information sources number and, thereby, with regard to factors taken into account during complex problem solving. Utilizing ontologies allows integration of the approach into existing processes and facilitates knowledge sharing with similar systems. Application of constraint networks allows rapid problem manipulation and usage of such existing efficient technologies as ILOG.

 
  • References

  • 1 The Future of Distributed Middleware: Grid Computing and Web Services, SIMC's General Meeting (November, 2002) URL. http://www.simcinc.org/archive0203/Grid/.
  • 2 De Roure D, Jennings NR, Shadbolt N. The Semantic Grid: A future e-Science infrastructure. International Journal of Concurrency and Computation: Practice and Experience, 2003. http://aspen.ucs.indiana.edu/CCPEwebresource/c590gridderoure/finalsource/c590semgridjournal-v5.doc.
  • 3 Smirnov A, Pashkin M, Chilov N, Levashova T. Knowledge Logistics in Information Grid Environment. The special issue “Semantic Grid and Knowledge Grid: The Next-Generation Web” (Zhuge H (ed.)) of International Journal on Future Generation Computer Systems. 2003; 20 (01) 61-79.
  • 4 Zhuge H. A Knowledge Grid Model and Platform for Global Knowledge Sharing. Expert Systems with Applications 2002; 22 (04) 313-20.
  • 5 Bolici F. D'Atri A, Fabi T, Vituzzi A. Health Grid Applications. Proceedings of the First European Health Grid Conference. Lyon, France: 2003: 212-6.
  • 6 Burke P, Piggott D. A Health Authority Strategic View of Regional Healthcare Networks and Transparent Access to Distributed Heterogonous Data. Proceedings of the First European Health Grid Conference. Lyon, France: 2003: 277-93.
  • 7 Smirnov A, Pashkin M, Chilov N, Levashova T, Haritatos F. Knowledge Source Network Configuration Approach to Knowledge Logistics. International Journal of General Systems. Taylor & Francis Group 2003; 32 (03) 251-69.
  • 8 Gruber T. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. IJHCS 1994; 43 (5/6) 907-28.
  • 9 Baumgaertel H. Distributed Constraint Processing for Production Logistics. IEEE Intelligent Systems 2000; 40-8.
  • 10 ILOG Corporate Web-site, 2003. http://www.ilog.com.
  • 11 Smirnov A, Pashkin M, Chilov N, Levashova T. Multi-Agent Knowledge Logistics System “KSNet”: Implementation and Case Study for Coalition Operations. Multi-Agent Systems and Applications. Proceedings of the 3rd International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2003 (Ma– ik V, Müller J, Pechouã ek M (eds.)). Proceedings LNAI 2691. Springer-Verlag, Berlin: Heidelberg; 2003: 292-303.
  • 12 Rathmell RA. A Coalition Force Scenario “Binni – Gateway to the Golden Bowl of Africa”. Proceedings on the International Workshop on Knowledge-Based Planning for Coalition Forces.. Tate A. (ed) 1999: 115-25.
  • 13 Clin-Act (Clinical Activity), The ON9.3 Library of Ontologies: Ontology Group of IP-CNR (a part of the Institute of Psychology of the Italian National Research Council (CNR)), 2000. http://saussure.irmkant.rm.cnr.it/onto/.
  • 14 Hpkb-Upper-Level-Kernel-Latest: Upper Cyc/ HPKB IKB Ontology with links to SENSUS, Version 1.4, February 1998. Ontolingua Ontology Server. http://www-ksl-svc.stanford.edu:5915.
  • 15 North American Industry Classification System code, DAML Ontology Library, Stanford University, July 2001. http://opencyc.sourceforge.net/daml/naics.daml.
  • 16 The UNSPSC Code (Universal Standard Products and Services Classification Code), DAML Ontology Library, Stanford University, January 2001. http://www.ksl.stanford.edu/projects/DAML/UNSPS.daml.