Artificial Intelligence in Medicine: 14th Conference on by Lucia Sacchi, Carla Rognoni, Stefania Rubrichi, Silvia

By Lucia Sacchi, Carla Rognoni, Stefania Rubrichi, Silvia Panzarasa, Silvana Quaglini (auth.), Niels Peek, Roque Marín Morales, Mor Peleg (eds.)

This ebook constitutes the refereed complaints of the 14th convention on synthetic Intelligence in drugs, AIME 2013, held in Murcia, Spain, in May/June 2013. The forty three revised complete and brief papers offered have been conscientiously reviewed and chosen from eighty two submissions. The papers are prepared within the following topical sections: determination aid, guidance and protocols; semantic expertise; bioinformatics; desktop studying; probabilistic modeling and reasoning; snapshot and sign processing; temporal facts visualization and research; and usual language processing.

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Extra resources for Artificial Intelligence in Medicine: 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29 – June 1, 2013. Proceedings

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The first one, the argument game, aims at collecting the agents’ arguments. The second game, the attack game, collects all the consensual and non-consensual attacks. The argument (resp. e. when they have no more arguments (resp. attacks) to push forward. Agents communicate by playing moves. The content of the moves depends on the subgame. Argument Game. Agents can submit new arguments. A move is well-formed iff it contains an argument (m = i, assert(a) with 1 ≤ i ≤ n and a ∈ i Ai ). Attack Game.

Drug-drug interactions, drug-disease interactions). As result, the HTN planner3 generates a TreeStub composed of a personalized, single-disease plan (with time-annotated primitive actions) and the rules of the planning domain applied to generate it. 3 For more information about the planning process, please see our previous work [4,5]. 26 I. S´ anchez-Garz´ on et al. 2 Conflict Solver : Plan Merging Strategy This module develops a plan merging process by combining the planning domains of two TreeStubs.

For instance, an unaccounted simultaneous constraint were found and modeled in MRO. We also used our CPG merging execution engine to execute CHF and AF CPG along with the instantiation of MRO. Domain expert believe that dynamically merging these two CPG enhances the relevance and timing of the recommendation as it does not make any assumptions about the execution flow of the CPG before execution and the decisions are made when the relevant information is available. Acknowledgements. This research project is sponsored by a research grant from Green Shield, Canada.

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