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The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment

Background  Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines. Objectives  As in many multi-agent...

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Detalles Bibliográficos
Autores principales: Lanzola, Giordano, Polce, Francesca, Parimbelli, Enea, Gabetta, Matteo, Cornet, Ronald, de Groot, Rowdy, Kogan, Alexandra, Glasspool, David, Wilk, Szymon, Quaglini, Silvana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499504/
https://www.ncbi.nlm.nih.gov/pubmed/37339683
http://dx.doi.org/10.1055/a-2113-4443
Descripción
Sumario:Background  Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines. Objectives  As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation. Methods  The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent. Results  The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production. Conclusion  The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.