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 e-book constitutes the refereed lawsuits of the 14th convention on synthetic Intelligence in medication, AIME 2013, held in Murcia, Spain, in May/June 2013. The forty three revised complete and brief papers provided have been rigorously reviewed and chosen from eighty two submissions. The papers are equipped within the following topical sections: selection help, instructions and protocols; semantic expertise; bioinformatics; computer studying; probabilistic modeling and reasoning; picture and sign processing; temporal information visualization and research; and normal language processing.

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Extra info 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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Thus, the architecture follows a life cycle that starting from a common specification of the main high-level steps of the treatment for a given patient, it will result in a personalized plan without harmful interactions. 2 Multi-agent Planning Architecture Figure 1(a) shows the proposed Multi-Agent Planning (MAP2 ) architecture that is composed of an Initiator Agent and several Planning Agents. A planning agent can be seen as the representation of a clinical specialist capable of (1) planning a personalized, single-disease care plan (HTN planner module); (2) coordinating with other specialists, by sharing its single-disease recommendations and experience (Coordinator module) and (3) detecting and resolving conflicts between its recommendations and those of others (Conflict Solver module).

Marín Morales, and M. ): AIME 2013, LNAI 7885, pp. 28–32, 2013. © Springer-Verlag Berlin Heidelberg 2013 Merging Disease-Specific Clinical Guidelines to Handle Comorbidities 29 clinically safe comorbid CPG. The tenets of our framework are as follows: (a) To computerize paper based CPG we use an existing OWL-based CPG ontology that models clinical tasks, diagnostic concepts, and therapeutic decisions [2]; (b) To represent the CPG merge criteria, we have developed an OWL-based Merge Representation Ontology (MRO) that captures the potential merge points between two or more CPG as per the judgment of a domain expert; and (c) To achieve CPG merging, we have augmented an already existing OWL-based execution engine [4] by OWL axioms and SWRL rules which calculate the effect of each CPG on others based on the merge criteria.

3. AGexp WPW Using CLP in Mitigating CPGs 21 invokes a CLP solver (we use the ECLi P S e system [7]) . No direct adverse interactions are detected so the model is augmented with the interaction operators described above and the solver is again invoked. This time, an adverse indirect interaction is detected, corresponding to interplay between the dosage of flecainide and oral amiodarone. 7, it translates to lowering from 200mg to 150mg). The revised model is passed to ECLi P S e and the solver produces the following solution: [WS0 = n, WS1 = n, WS2 = n, DF0 = 50, DF1 = 100, DF2 = 150, DF3 = 200, DF= 150, HI = y, EC = true, RAE = y, PD = true] that represents a combined therapy.

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