Titlebook: Artificial Intelligence in Medicine; 11th Conference on A Riccardo Bellazzi,Ameen Abu-Hanna,Jim Hunter Conference proceedings 2007 Springer

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Eli Biham,Orr Dunkelman,Nathan Kellern system failure status. The method also explicitly avoids the collinearity problem among organ failure episodes. We report on the application of our method to a large dataset and demonstrate its added value. The ubiquity of severity scores and sub-scores in medicine renders our approach relevant to a wide range of medical domains.

Rodent

Discovery and Integration of Organ-Failure Episodes in Mortality Predictionn system failure status. The method also explicitly avoids the collinearity problem among organ failure episodes. We report on the application of our method to a large dataset and demonstrate its added value. The ubiquity of severity scores and sub-scores in medicine renders our approach relevant to a wide range of medical domains.

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A Human-Machine Cooperative Approach for Time Series Data Interpretationdue to the temporal nature of the data at hand, and to the context-sensitive aspect of data interpretation, which hamper the formulation of . knowledge about the kind of patterns to detect and their interrelations. A new way to tackle this problem is proposed, based on a collaborative approach betwe

Toxoid-Vaccines

MRF Agent Based Segmentation: Application to MRI Brain Scansans. Most MRF approaches handle tissues segmentation via global model estimation. Structure segmentation is then carried out as a separate task. We propose in this paper to consider MRF segmentation of tissues and structures as two local and cooperative procedures immersed in a multiagent framework.

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R-CAST-MED: Applying Intelligent Agents to Support Emergency Medical Decision-Making Teamscally-dispersed teams in an environment characterized by insufficient information, ineffective collaboration and inadequate resources. Despite the increasing adoption of decision support systems in healthcare, there is limited evidence of their value in large-scale disasters. We conducted focus grou

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Knowledge-Based Modeling and Simulation of Diseases with Highly Differentiated Clinical Manifestatioenvironment. GERD represents a class of diseases that have a large number of clinical manifestations. Our model at once manages that complexity while offering robust automatic function in response to open-ended user actions. This ontologically grounded model is largely based on script-oriented repre

MAL

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