Cousins S B, Frisse M E, Chen W, Mead C N
Proc Annu Symp Comput Appl Med Care. 1991:696-700.
One of the barriers to using belief networks for medical information retrieval is the computational cost of reasoning as the networks become large. Stochastic simulation algorithms allow one to compute approximations of probability values in a reasonable amount of time. We previously examined the performance of five stochastic simulation algorithms applied to four simple belief networks networks and found that the Self-Importance algorithm performed well. In this paper, we examine how the same five algorithms perform when applied to a belief network derived from the cardiovascular subtree of the Medical Subject Headings (MeSH). Both the Likelihood Weighting and Self-Importance algorithms perform well when applied to the MeSH-derived network, suggesting that stochastic simulation algorithms may provide reasonable performance in medical information retrieval settings.
将信念网络用于医学信息检索的障碍之一是随着网络规模变大，推理的计算成本也会增加。随机模拟算法能让人在合理时间内计算概率值的近似值。我们之前研究了应用于四个简单信念网络的五种随机模拟算法的性能，发现自重要性算法表现良好。在本文中，我们研究了将相同的五种算法应用于从医学主题词表（MeSH）的心血管子树派生的信念网络时的性能。当应用于从MeSH派生的网络时，似然加权算法和自重要性算法都表现良好，这表明随机模拟算法在医学信息检索环境中可能提供合理的性能。
