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Markov model and meta-heuristics combined method for cost-effectiveness analysis
Flexible Services and Manufacturing Journal ( IF 2.5 ) Pub Date : 2019-09-30 , DOI: 10.1007/s10696-019-09369-0
Xiuxian Wang , Na Geng , Jianxin Qiu , Zhibin Jiang , Liping Zhou

Cost-effectiveness analysis is an important topic in public health, which can provide valuable information for medical decisions. Several modeling methods are available for conducting cost-effectiveness analysis. However, it is difficult when the data is incomplete. To solve this problem, a Markov model is proposed to model patients’ health states transition, and two hybrid metaheuristics are proposed to estimate the transition probabilities. Based on the estimated transition probabilities, cost-effectiveness analysis is conducted to compare different medical interventions. Numerical experiments and case study validate the effectiveness and practicability of the proposed method. The case study gives the physicians effective instructions by comparing two different immunosuppressants after renal transplantation.

中文翻译:

马尔可夫模型与元启发式组合方法进行成本效益分析

成本效益分析是公共卫生领域的重要主题,可以为医疗决策提供有价值的信息。有几种建模方法可用于进行成本效益分析。但是,当数据不完整时很难。为了解决这个问题,提出了一个马尔可夫模型来模拟患者的健康状态转变,并提出了两种混合元启发式方法来估计转变概率。基于估计的过渡机率,进行成本效益分析以比较不同的医疗干预措施。数值实验和案例研究验证了该方法的有效性和实用性。案例研究通过比较肾移植后两种不同的免疫抑制剂为医生提供了有效的指导。
更新日期:2019-09-30
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