Active pooling design in group testing based on Bayesian posterior prediction
Active pooling design in group testing based on Bayesian posterior prediction
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基于贝叶斯后验预测的分组测试主动池化设计
DOI：
10.1103/physreve.103.022110
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发表时间：
2021
影响因子：
2.4
通讯作者：
Ayaka Sakata
中科院分区：
文献类型：
--
作者：
Chanathip Pornprasit;Xin Liu;Natthawut Kertkeidkachorn;Kyoung-Sook Kim;Thanapon Noraset;Suppawong Tuarob;Ayaka Sakata
在人群中识别感染患者时，群体检测是减少检测次数和纠正检测错误的有效方法。在分组检测中，对采集自患者的样本池进行检测，样本池数量低于患者数量。分组检测的性能在很大程度上取决于用于从检测结果推断受感染患者的池和算法的设计。本文在贝叶斯推理框架下，提出了一种基于预测分布的自适应游泳池设计方法。所提出的方法，执行使用的信念传播算法，结果在更准确地识别感染的患者相比，提前确定的随机池进行的组测试。
For identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct test errors. In group testing, tests are performed on pools of specimens collected from patients, where the number of pools is lower than that of patients. The performance of group testing considerably depends on the design of pools and algorithms that are used for inferring the infected patients from the test outcomes. In this paper, an adaptive design method of pools based on the predictive distribution is proposed in the framework of Bayesian inference. The proposed method, executed using a belief propagation algorithm, results in more accurate identification of the infected patients compared with the group testing performed on random pools determined in advance.