Contrasting temporal trend discovery for large healthcare databases
Contrasting temporal trend discovery for large healthcare databases
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DOI：
10.1016/j.cmpb.2013.09.005
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发表时间：
2014-01-01
影响因子：
6.1
通讯作者：
Ojstersek, Milan
中科院分区：
文献类型：
--
作者：
Hrovat, Goran;Stiglic, Gregor;Ojstersek, Milan
随着人们对电子健康记录的接受程度的提高，我们可以观察到数据挖掘方法在这一领域的应用越来越受到关注。本文介绍了一种新的方法，探索和比较不同的住院病人亚组的时间趋势，这是基于关联规则挖掘使用Apriori算法和线性模型为基础的递归分割。全国住院病人样本（NIS），医疗保健成本和利用项目（HCUP），医疗保健研究和质量机构被用来评估所提出的方法。这项研究提出了一种新的方法，其中大数据的可视化分析用于以回归树的形式进行趋势发现，树的叶子中有散点图。趋势线用于直接比较指定时间范围内的线性趋势。我们的研究结果表明，存在相反的趋势，年龄和性别为基础的亚组，这将是不可能发现使用传统的趋势跟踪技术。这种方法可以用于决策者在组织活动时的决策支持应用程序，或者用于医院管理层观察使用传统分析技术无法直接发现的趋势。(C)2013爱思唯尔爱尔兰有限公司版权所有。
With the increased acceptance of electronic health records, we can observe the increasing interest in the application of data mining approaches within this field. This study introduces a novel approach for exploring and comparing temporal trends within different in-patient subgroups, which is based on associated rule mining using Apriori algorithm and linear model-based recursive partitioning. The Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality was used to evaluate the proposed approach. This study presents a novel approach where visual analytics on big data is used for trend discovery in form of a regression tree with scatter plots in the leaves of the tree. The trend lines are used for directly comparing linear trends within a specified time frame. Our results demonstrate the existence of opposite trends in relation to age and sex based subgroups that would be impossible to discover using traditional trend-tracking techniques. Such an approach can be employed regarding decision support applications for policy makers when organizing campaigns or by hospital management for observing trends that cannot be directly discovered using traditional analytical techniques. (C) 2013 Elsevier Ireland Ltd. All rights reserved.