Hazen G B
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119.
Med Decis Making. 1993 Jul-Sep;13(3):227-36. doi: 10.1177/0272989X9301300309.
The stochastic tree is a continuous-time version of a Markov-cycle tree, useful for constructing and solving medical decision models in which risks of mortality and morbidity may extend over time. Stochastic trees have advantages over Markov-cycle trees in graphic display and computational solution. Like the decision tree or Markov-cycle tree, stochastic tree models of complex medical decision problems can be too large for convenient graphic formulation and display. This paper introduces the notion of factoring a large stochastic tree into simpler components, each of which may be easily displayed. It also shows how the rollback solution procedure for unfactored stochastic trees may be conveniently adapted to solve factored trees. These concepts are illustrated using published examples from the medical literature.
随机树是马尔可夫循环树的连续时间版本，对于构建和求解死亡率和发病率风险可能随时间延伸的医学决策模型很有用。随机树在图形显示和计算求解方面比马尔可夫循环树具有优势。与决策树或马尔可夫循环树一样，复杂医学决策问题的随机树模型可能太大，难以进行方便的图形化表述和显示。本文介绍了将大型随机树分解为更简单组件的概念，每个组件都可以轻松显示。它还展示了如何方便地调整未分解随机树的回退求解过程来求解分解后的树。这些概念通过医学文献中已发表的示例进行说明。
