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Effective patient prioritization in mass casualty incidents using hyperheuristics and the pilot method

机译:使用启发式方法和先导方法对大规模伤亡事件进行有效的患者优先排序

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摘要

Whenever a mass casualty disaster takes place, the medical infrastructure available has to deal with a surge in the number or patients severely ill or injured. Using triage methods casualties have to be prioritized to receive health care in a limited-resource scenario. Aiming to do the greatest good to the greatest number of people, it has to be determined how to make the best use of these resources. This constitutes a very complex task that has to consider issues such as the current number of casualties, their lifetime expectancy, their resource consumption, etc. We approach this task within the framework of the pilot method and hyperheuristics. We show how these metaheuristics can effectively manage a number of simpler heuristics, providing improved results on an ample set of simulated problem scenarios. An exhaustive empirical evaluation analyzes the influence on performance of factors such as the total number of casualties, the severity of their medical condition, the treatment time, the number of resources available, or the number of triage classes.
机译:每当发生大规模人员伤亡灾难时,可用的医疗基础设施就必须应对数量激增或严重生病或受伤的患者。使用分流方法,必须优先考虑伤亡情况,以便在资源有限的情况下获得医疗服务。为了最大程度地为最大数量的人谋福利,必须确定如何最有效地利用这些资源。这是一项非常复杂的任务,必须考虑诸如当前伤亡人数,其预期寿命,其资源消耗等问题。我们在试点方法和超启发式方法的框架内处理此任务。我们将展示这些元启发式方法如何有效管理许多简单的启发式方法,并在大量模拟问题场景中提供改进的结果。详尽的经验评估分析了对诸如人员伤亡总数,其病情严重程度,治疗时间,可用资源数量或分类分类数量等因素对绩效的影响。

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