Gleaning data from disaster: a hospital-based data mining method to study all-hazard triage after a chemical disaster.
Gleaning data from disaster: a hospital-based data mining method to study all-hazard triage after a chemical disaster.
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DOI：
10.5055/ajdm.2013.0116
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发表时间：
2013-01-01
影响因子：
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通讯作者：
Svendsen, Erik R
中科院分区：
文献类型：
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作者：
Craig, Jean B;Culley, Joan M;Svendsen, Erik R
目的：描述评估当前可用的分流模型的方法，以评估其在所有危险灾难后对激增的患者进行适当分流的有效性。设计：开发了一种方法，用于评估当前可用的分流模型，使用从Graniteville氯气灾难受害者的医疗记录中提取的数据。2005年1月6日，一列载有三节液氯罐车汽车的货运列车无意中驶入南卡罗来纳州格拉尼特维尔市中心的一条工业支线，随后撞上了一辆停着的机车并脱轨。这导致其中一艘氯气油轮破裂，立即释放出约60吨氯气。氯气渗透到人口7,000的城镇。参与者：本研究的重点是在南卡罗来纳州接受紧急护理的受害者。结果：通过我们的数据映射和决策树逻辑，作者成功地使用了可用的提取的临床数据来估计我们研究中使用的分诊类别。本文中概述的方法显示了精心设计的二次分析方法的潜在用途，以改善大规模伤亡研究。这些步骤是可靠和可重复的，可以很容易地扩展或应用到其他灾难数据集。
OBJECTIVE: To describe the methods of evaluating currently available triage models for their efficacy in appropriately triaging the surge of patients after an all-hazards disaster.DESIGN: A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.SETTING: On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, SC. The train then crashed into a parked locomotive and derailed. This caused one of the chlorine tankers to rupture and immediately release ~60 tons of chlorine. Chlorine gas infiltrated the town with a population of 7,000.PARTICIPANTS: This research focuses on the victims who received emergency care in South Carolina.RESULTS: With our data mapping and decision tree logic, the authors were successful in using the available extracted clinical data to estimate triage categories for use in our study.CONCLUSIONS: The methodology outlined in this article shows the potential use of well-designed secondary analysis methods to improve mass casualty research. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.