Riccardo Bellazzi, Ameen Abu-Hanna, Jim Hunter's Artificial Intelligence in Medicine: 11th Conference on PDF

By Riccardo Bellazzi, Ameen Abu-Hanna, Jim Hunter

ISBN-10: 3540735984

ISBN-13: 9783540735984

This publication constitutes the refereed court cases of the eleventh convention on man made Intelligence in medication in Europe, AIME 2007, held in Amsterdam, The Netherlands in July 2007.

The 28 revised complete papers and 38 revised brief papers awarded have been rigorously reviewed and chosen from 137 submissions. The papers are geared up in topical sections on agent-based structures, temporal facts mining, desktop studying and information discovery, textual content mining, traditional language processing and iteration, ontologies, selection aid structures, functions of AI-based photo processing options, protocols and directions, in addition to workflow systems.

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Keywords: Intelligent Agent, Simulation, Mass Casualty Incidents, Decision Support Systems, Information Management. 1 Introduction Emergency medical decision-making is complex, especially during mass casualty incidents. During a Mass Casualty Incident (MCI), hospitals are required to deal with a large influx of patients with various levels of trauma in a short period of time [1]. The key goals for healthcare providers during such MCIs include rapid evacuation of patients from the incident site, and quick provision of critical medical care to a large number of patients [2], etc.

RPD model [11] describes how experienced decision makers make decisions under time pressure in real situations. It argues that human experts usually make decisions based on their past experiences. They select an experience that worked before for similar situations, instead of calculating and comparing expected utility for each decision choice. SMM is a hypothetical cognitive construct that refers to a common understanding among team members regarding their objectives, roles, knowledge etc. SMM attempts to explain many of the human behaviors in high performance teams [12].

It should be clear that many aspects of GERD are inherent parts of the disease model that cannot be modified by patient authors. For example, a patient with a GERD level of 2 will have a faster-progressing disease than a patient with a GERD level of 9, assuming no effective treatment. 8 By contrast, there are practical and pedagogical reasons to permit different patients to display other kinds of differences provided for in the patient authoring process. 3 Patient Authoring Patient authoring involves filling in a one-page on-line questionnaire that permits domain experts or teachers to create a population of patients that display clinically relevant variations on the disease theme.

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Artificial Intelligence in Medicine: 11th Conference on Artificial Intelligence in Medicine in Europe, AIME 2007, Amsterdam, The Netherlands, July 7-11, by Riccardo Bellazzi, Ameen Abu-Hanna, Jim Hunter


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