State University of New York - Binghamton, School of Management, P.O. Box 6000, Binghamton, NY 13902, USA.
Math Biosci. 2010 Jul;226(1):28-37. doi: 10.1016/j.mbs.2010.03.007. Epub 2010 Mar 31.
Many papers in the medical literature analyze the cost-effectiveness of screening for diseases by comparing a limited number of a priori testing policies under estimated problem parameters. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop and solve a Markov Decision Process (MDP) model for a simple class of asymptomatic diseases in order to provide the building blocks for analysis of a more general class of diseases. We provide a computationally efficient method for determining a cost-effective dynamic intervention strategy that takes into account (i) the results of the previous test for each individual and (ii) the change in the individual's behavior based on awareness of the disease. We demonstrate the usefulness of the approach by applying the results to screening decisions for Hepatitis C (HCV) using medical data, and compare our findings to current HCV screening recommendations.
许多医学文献中的论文通过比较估计问题参数下有限数量的先验测试策略来分析疾病筛查的成本效益。然而，这可能不足以确定测试的最佳时间或纳入随时间的变化。在本文中，我们开发并解决了一个简单类无症状疾病的马尔可夫决策过程（MDP）模型，以便为更一般类疾病的分析提供构建模块。我们提供了一种计算有效的方法来确定一种具有成本效益的动态干预策略，该策略考虑了（i）每个人的前一次测试结果和（ii）基于对疾病的认识而改变的个人行为。我们通过使用医疗数据对丙型肝炎（HCV）的筛查决策应用结果来证明该方法的有用性，并将我们的发现与当前的 HCV 筛查建议进行比较。
