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首页> 外文会议>The 19th International Conference on Information Quality,Big Data: management amp; Data quality >A Multi-tiered Medical Data-Mining and Visualization Framework for Rule Investigations
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A Multi-tiered Medical Data-Mining and Visualization Framework for Rule Investigations

机译：用于规则调查的多层医学数据挖掘和可视化框架

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Hospital ED crowding has led to an increase in patients’ waiting times; thus, solving this problem requiresrna better understanding of the hospital’s patient flow and the behaviors of patients. Existing research onrnED crowding is sparse and has tended to focus on the present crowding state. Recent researches havernaddressed the importance of analyzing the length of stay (LOS) to understand the behaviors of patients inrnthe ED, and these provide a good departure point for understanding patients’ behaviors based on the LOSrnfactor. In our ongoing work, we proposed a domain-driven, ED-crowding data-mining approach torninvestigate the relationship between various types of patient behaviors and their LOS and to build a modelrnto predict patients’ LOS. The objective of this study is to build an interactive decision support systemrn(DSS) for Mackay Memorial Hospital, which has the second-largest ED in Taiwan and is a representativerninstitute. Accordingly, the contributions of this study are (1) building the DSS based on the proposedrndomain-driven medical data-mining process in the ED and (2) visualizing the extracting rules and thernstatistical data in the proposed rule-based medical decision support (R-MDS) visualization portal. Wernintroduce the system framework with associated modules in this study. We aim to integrate domainrnknowledge of the hospital ED with the data-mining technique to develop the system and providerninteractive DSS using modern visualization techniques. We also believed that the qualified rules can bernvalidated effectively and efficiently by experts with the aid of the proposed framework.
机译：医院急诊部的拥挤导致患者的等待时间增加。因此，解决此问题需要更好地了解医院的病人流量和病人的行为。关于ED拥挤的现有研究很少，并且倾向于集中于当前的拥挤状态。最近的研究已经解决了分析住院时间（LOS）的重要性，以了解ED患者的行为，这为了解基于LOSrn因素的患者行为提供了一个很好的出发点。在我们正在进行的工作中，我们提出了一种域驱动的ED拥挤数据挖掘方法，以研究各种类型的患者行为与其LOS之间的关系，并建立一个模型来预测患者的LOS。这项研究的目的是为麦凯纪念医院建立一个交互式决策支持系统（DSS），该系统是台湾第二大ED，是该研究所的代表。因此，这项研究的贡献是（1）基于ED中提议的rndomain驱动的医学数据挖掘过程构建DSS，（2）在提议的基于规则的医学决策支持（R）中可视化提取规则和统计数据-MDS）可视化门户。在本研究中，Wern引入了具有相关模块的系统框架。我们旨在将医院ED的领域知识与数据挖掘技术相集成，以使用现代可视化技术开发系统和提供者交互式DSS。我们还认为，在建议的框架的帮助下，专家可以有效地验证合格的规则。

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