Agent Technology and e-Health by Roberta Annicchiarico, Ulises Cortes Garcia, Cristina

By Roberta Annicchiarico, Ulises Cortes Garcia, Cristina Urdiales

Multi-agent platforms are probably the most fascinating examine components in man made Intelligence. This e-book experiences at the effects accomplished during this sector, discusses the advantages (and drawbacks) that agent-based structures may well convey to clinical domain names and society, and in addition presents a listing of the learn subject matters that are meant to be tackled within the close to destiny to make the deployment of health-care agent-based structures a fact. present subject matters of study contain conversation and co-operation among allotted clever brokers to control sufferer care.

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The details of this extension can be found in [31, 32]. The Agent Factory Agent processes the clinical guideline definitions represented in our extended model, and based on the semantic annotations of the external resources, discovers the instances of the specified resources that are relevant for our specific patient. This process can be summarized as follows: • In SAPHIRE architecture, the medical Web services exposing functionalities of healthcare information systems, and also the sensor Web services exposing the sensor data retrieved from wireless medical sensor devices are published to a UDDI registry by annotating them with their functionality semantics.

1. Provenance Architecture The provenance architecture is comprised of component interfaces, data models, protocols and agent behaviour specifications. Following this approach, each agent independently records documentation regarding the processes it is involved in. The documentation is structured in a form which then allows queriers to trace back through the full, distributed process that preceded a data item’s creation or modification. The provenance architecture has key properties which allow for its wide applicability, scalability and robustness.

Zeng, A. Greenes, V. Patel, and E. Shortliffe, “Design and Implementation of GLIF3 guideline execution engine,” Journal of Biomedical Informatics,, vol. 37, pp. 305–318, 2004. [8] P. Terenziani, G. Molino, and M. Torchio, “A modular approach for representing and executing clinical guidelines,” Artificial Intelligence in Medicine,, vol. 23(3), pp. 249–276, 2001. [9] Y. Shahar, O. Young, E. Shalom, M. Galperin, A. Mayaffit, R. Moskovitch, and A. Hessing, “ A Framework for a Distributed, Hybrid, Multiple-Ontology ClinicalGuideline Library and Automated Guideline-Support Tools,” Journal of Biomedical Informatics,, vol.

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