Abstract
This work addresses the generation of a personalized treatment plan from multiple clinical guidelines, for a patient with multiple diseases (comorbid patient), as a multi-agent cooperative planning process that provides support to collaborative medical decision-making. The proposal is based on a multi-agent planning architecture in which each agent is capable of (1) planning a personalized treatment from a temporal Hierarchical Task Network (HTN) representation of a single-disease guideline, and (2) coordinating with other planning agents by both sharing disease specific knowledge, and resolving the eventual conflicts that may arise when conciliating different guidelines by merging single-disease treatment plans. The architecture follows a life cycle that starting from a common specification of the main high-level steps of a treatment for a given comorbid patient, results in a detailed treatment plan without harmful interactions among the single-disease personalized treatments.
Work partially supported by projects P08-TIC-3572 and TIN2011-27652-C03-03/01.
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Sánchez-Garzón, I., Fdez-Olivares, J., Onaindía, E., Milla, G., Jordán, J., Castejón, P. (2013). A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_4
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DOI: https://doi.org/10.1007/978-3-642-38326-7_4
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