摘要翻译：
在过去的一年里，超过7万人因严重受伤被送往英国医院。每一次，临床医生都必须紧急地对病人进行筛查，以便对创伤治疗做出可靠的决定。通常，这样的过程包括大约20个测试；然而，创伤病人的情况仍然很难得到适当的测试。如果这些测试被含糊不清地解释，关于损伤严重程度的信息会产生误导，会发生什么？决策中的错误可能是致命的：使用温和的治疗会使患者面临死于创伤后休克的风险，而使用过度治疗也会导致死亡。我们怎样才能降低不可靠的决策所导致的死亡风险？已有研究表明，基于贝叶斯决策模型平均方法的概率推理允许临床医生评估决策中的不确定性。在此基础上，本文的目标是选择最重要的筛选测试，保持较高的性能。我们假设贝叶斯方法中的概率推理允许我们发现筛选测试和决策不确定性之间的新关系。在实践中，选择信息最丰富的测试也可以降低创伤护理中心筛查程序的成本。在我们的实验中，我们使用英国创伤数据来比较提出的技术在性能方面的效率。我们还比较了决策的不确定性方面的熵。
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英文标题：
《Feature Selection for Bayesian Evaluation of Trauma Death Risk》
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作者：
L. Jakaite and V. Schetinin
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In the last year more than 70,000 people have been brought to the UK hospitals with serious injuries. Each time a clinician has to urgently take a patient through a screening procedure to make a reliable decision on the trauma treatment. Typically, such procedure comprises around 20 tests; however the condition of a trauma patient remains very difficult to be tested properly. What happens if these tests are ambiguously interpreted, and information about the severity of the injury will come misleading? The mistake in a decision can be fatal: using a mild treatment can put a patient at risk of dying from posttraumatic shock, while using an overtreatment can also cause death. How can we reduce the risk of the death caused by unreliable decisions? It has been shown that probabilistic reasoning, based on the Bayesian methodology of averaging over decision models, allows clinicians to evaluate the uncertainty in decision making. Based on this methodology, in this paper we aim at selecting the most important screening tests, keeping a high performance. We assume that the probabilistic reasoning within the Bayesian methodology allows us to discover new relationships between the screening tests and uncertainty in decisions. In practice, selection of the most informative tests can also reduce the cost of a screening procedure in trauma care centers. In our experiments we use the UK Trauma data to compare the efficiency of the proposed technique in terms of the performance. We also compare the uncertainty in decisions in terms of entropy.
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PDF链接：
https://arxiv.org/pdf/0805.3802