Markov Models in Medical Decision Making: A Practical Guide

This journal explains how Markov Method can be applied on health sector. Markov Method used to give decision probability concern with patient treatment. A Markov model may be evaluated by matrix algebra, as a cohort simulation, or as a Monte Carlo simulation.

A newer representation of Markov models, the Markov-cycle tree, uses a tree representation of clinical events and may be evaluated either as a cohort simulation or as a Monte Carlo simulation. Algebra matrix needs less estimation but only can use in condition that probability transition have a constant value.

This state is one of Markov process called Markov. Markov model is a solution in medical which the risk happened as a continue event, more than one event and the probability next event depend on previous event. The ability of the Markov model to represent repetitive events and the time dependence of both probabilities and utilities allows for more accurate representation of clinical settings that involve these issues.

It has explain before that Markov model is used for problem decision making which have risk depend on the time or ongoing over time. Then, Markov model is used to analyze the risk of hemorrhage while the patient on anticoagulant therapy, and the risk of rupture of an abdominal aortic aneurysm.

There are two consequences on this problem. First this event as happened in uncertain time. For example a stroke that occurs immediately may have a different impact on a patient. Second, this event may happen more than once. However, this problem can be represented by Cohort Simulation, Monte Carlo Simulation or Algebra Matrix.

Markov Models in Medical Decision Making: A Practical Guide written by Frank A. Sonnenberg and J. Robert Beck.

Written by : Ardian Rinto