Get Markov Models In Medical Decision Making:

Historically, two models of clinical decision making are recognized from the literature; the information-processing model and the intuitive-humanist model.

Historically, two models of clinical decision making are recognized from the literature; the information-processing model and the intuitive-humanist model.

A Markov model is a stochastic method for randomly changing systems that possess the Markov property. This means that, at any given time, the next state is only dependent on the current state and is independent of anything in the past.

Markov models assume that a patient is always in one of a finite number of discrete health states, called Markov states. All events are represented as transitions from one state to another. A Markov model may be evaluated by matrix algebra, as a cohort simulation, or as a Monte Carlo simulation.

In health sciences, Markov models are widely used as analytical tools to assess diseases from an economic point of view. ing to this technique, a patient may be assessed in a finite number of discrete states of health, in which the important clinical events are modeled as transitions from one state to another.

The Markov analysis process involves defining the likelihood of a future action, given the current state of a variable. Once the probabilities of future actions at each state are determined, a decision tree can be drawn, and the likelihood of a result can be calculated.

The Markov analysis process involves defining the likelihood of a future action, given the current state of a variable. Once the probabilities of future actions at each state are determined, a decision tree can be drawn, and the likelihood of a result can be calculated.

Examples of health states that might be included in a simple Markov model for a cancer intervention are: progression-free, post-progression and dead. Individuals move ('transition') between disease states as their condition changes over time.