Dittus R S, Roberts S D, Wilson J R
Department of Medicine, Regenstrief Institute for Health Care, Indiana University School of Medicine, Indianapolis 46202.
J Am Coll Cardiol. 1989 Sep;14(3 Suppl A):23A-28A. doi: 10.1016/0735-1097(89)90158-7.
Effective handling of uncertainty is one of the central problems in medical decision making. The sources and effects of uncertainty in medical decision making are examined and some new quantitative approaches for solving the associated problems are outlined. To handle uncertainty in the branching probabilities and node utilities for probability trees representing alternative treatment strategies, a public domain software package that can be used for the construction, analysis and comparison of probability trees with random parameters was developed. To facilitate specification of the random variables that arise in medical decision making problems, public domain software packages for both data-driven and subjective estimation of probability densities from the Johnson translation system of distributions have also been developed. For the analysis of complex problems that cannot be adequately represented by probability trees or by simple stochastic processes such as Markov chains, network simulation approaches that are oriented toward the sequence of activities seen by individual patients in the course of treatment are described.
有效处理不确定性是医学决策中的核心问题之一。本文探讨了医学决策中不确定性的来源和影响，并概述了一些解决相关问题的新定量方法。为了处理表示替代治疗策略的概率树的分支概率和节点效用中的不确定性，开发了一个可用于构建、分析和比较具有随机参数的概率树的公共领域软件包。为了便于指定医学决策问题中出现的随机变量，还开发了用于从约翰逊分布转换系统进行数据驱动和主观概率密度估计的公共领域软件包。对于无法用概率树或马尔可夫链等简单随机过程充分表示的复杂问题，描述了面向个体患者在治疗过程中所经历的活动序列的网络模拟方法。
