An entropy-based algorithm for detecting clusters of cases and controls and its comparison with a method using nearest neighbours
An entropy-based algorithm for detecting clusters of cases and controls and its comparison with a method using nearest neighbours
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DOI：
10.1016/s1353-8292(97)00026-9
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发表时间：
1998-03-01
期刊：
影响因子：
4.8
通讯作者：
Swartz, Joel B.
中科院分区：
文献类型：
--
作者：
Swartz, Joel B.
提出了一种基于熵的疾病聚类检测方法。对于这种方法，病例和对照被绘制在地图上。地图被划分为区域。空间的熵计算为在给定病例和对照的总数以及每个区域中的病例和对照的数量的情况下在各个区域中放置病例和对照的可能方式的数量的对数。熵技术的功率测试对最近邻技术（NNT）的功率。熵的方法被证明是比NNT更强大，当有一个以上的集群在空间中，或当集群的边界附近的空间。(C)1998爱思唯尔科技有限公司版权所有
A new method for detecting disease clustering based on entropy is presented. For this method cases and controls are plotted on a map. The map is divided into regions. The entropy of the space is calculated as the log of the number of possible ways of placing the cases and controls in the various regions given the total number of cases and controls and the number of cases and controls in each region. The power of the entropy technique is tested against the power of the nearest neighbour technique (NNT). The entropy method is shown to be substantially more powerful than the NNT when there is more than one cluster in the space or when the clusters are near the boundary of the space. (C) 1998 Elsevier Science Ltd. All rights reserved