Application of Bayesian Statistics and Markov Model in Medical Decision

Abstract:

Bayesian statistics is another scientific method for statistics besides classical statistics which makes use of the prior information mixed with the sample information to draw some inferences about the posterior distributions of parameters based on the Bayesian formula. The Markov process is a random one transiting from one state to another. The models based on Markov process match well to the medical clinical practice. The medical intervention measures need to be evaluated not only the clinical effect and safety, but also their cost-utility. We study the application of evaluating the cost-utility of medical intervention measures based on analysis of Bayesian statistics and Markov model hoping for providing medical decision-making reference.

• Periodical: Applied Mechanics and Materials (Volumes 195-196)
• Pages: 318-323
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.195-196.318
• Online since: August 2012
• Authors: Jian Qian Chao, Yi Min Wang, Wen Yuan Xie, Lin Tian, Jing Jing Zhang
• Keywords: Bayesian Statistics, Cost-Utility, Markov Model, Medical Decision-Making

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