Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software
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Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3—a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.
KeywordsClinical decision support Agent-based systems Open source systems JADE Protégé
The research presented here was supported by NSERC-CIHR program. The authors would like to thank the MET3 programming team of Tomasz Buchert, Bartosz Kukawka, and Tomasz Maciejewski. The second author acknowledges the support of the Polish Ministry of Science and Higher Education (grant N N519 314435).
- 2.Patel, V., Arocha, J. F., and Zhang, J., Thinking and reasoning in medicine. In: Holyoak, K., and Morrison, R. (Eds.), The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press, Cambridge, pp. 727–50, 2005.Google Scholar
- 3.Field, M. J., and Lohr, K. N. (Eds.), Guidelines for Clinical Practice: From Development to Use. National Academy Press, Washington, D.C., 1992.Google Scholar
- 4.The Cochrane library. http://www.thecochranelibrary.com/.
- 8.Wilk, S., Michalowski, W., O’Sullivan, D., Farion, K., and Matwin, S., Engineering of a clinical decision support framework for the point of care use. AMIA Annu. Symp. Proc. 6:814–818, 2008.Google Scholar
- 10.Michalowski, W., Slowinski, R., Wilk, S., Farion, K., Pike, J., and Rubin, S., Design and development of a mobile system for supporting emergency triage. Methods Inf. Med. 44(1):14–24, 2005.Google Scholar
- 11.Moreno, A., Valls, A., Riaño, and D. PalliaSys: Agent-based proactive monitoring of palliative patients. In proceedings of IWPAAMS, Oct 20–21, León, Spain, 2005.Google Scholar
- 12.Su, C., and Wua, C., JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring. Applied Soft Computing (In press).Google Scholar
- 14.Hudson, D. L., and Cohen, M. E., Use of intelligent agents in the diagnosis of cardiac disorders. Comput. Cardiol. 633–636, 2002.Google Scholar
- 16.Hashmi, Z. I., Abidi, S. S. R., and Cheah, Y., An intelligent agent-based knowledge broker for enterprise-wide healthcare knowledge procurement. In Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems. Maribor, Slovenia, 173, 2002.Google Scholar
- 19.Hogarth, M. A., and Turner, S., A study of clinically related open source software projects. Proceedings of AMIA Symposium, 330–334, 2005.Google Scholar
- 21.Chu, S. C., From component-based to service oriented software architecture for healthcare. In Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry. HEALTHCOM 96–100, 2005.Google Scholar
- 22.Weiss G., A modern approach to distributed artificial intelligence. MIT, 1999.Google Scholar
- 24.Garcia-Ojeda, J. C., DeLoach, S. A., Robby, Oyenan, W. H., and Valenzuela, J., O-MaSE: a customizable approach to developing multi-agent development processes. In: Luck, M. (Ed.), Agent-oriented software engineering VIII: the 8th International Workshop on Agent Oriented Software Engineering (AOSE). Springer-Verlag, Berlin, pp. 1–15, 2008.Google Scholar
- 26.Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., and Mylopoulos, J., TROPOS: An agent-oriented software development methodology. In Journal of Autonomous Agents and Multi-Agent Systems. Kluwer Academic Publishers, May 2004.Google Scholar
- 27.Iglesias, C. A., Garijo, M., Centeno-González, J., and Velasco, J. R., Analysis and design of multiagent systems using MAS-Common KADS, Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages, Lecture Notes In Computer Science, vol. 1365. Springer-Verlag London, UK, pp. 313–327, 1997.Google Scholar
- 28.Foster, I., Jennings, N. R., and Kesselman C., Brain meets brawn: why Grid and agents need each other. In Proceedings 3rd International Conference on Autonomous Agents and Multi-Agent Systems, New York, US, 2004.Google Scholar
- 31.Michalowski, W., Wilk, S., Farion, K., Pike, J., Rubin, S., and Slowinski, R., Development of a decision algorithm to support emergency triage of scrotal pain and its implementation in the MET system. INFOR 43(4):287–301, 2005.Google Scholar
- 32.Farion, K., Michalowski, W., Wilk, S., O’Sullivan, D., and Matwin, S., A tree-based decision model to support prediction of the severity of asthma exacerbations in children. J Med Syst, 2009 (in press).Google Scholar
- 34.Canadian Association of Emergency Physicians: Guidelines for Emergency Management of Paediatric Asthma. Available at http://www.caep.ca/.
- 35.Bellifemine, F. L., Caire, G, and Greenwood, D., Developing multi-agent systems with JADE. Wiley, 2004.Google Scholar