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The medical profession leads physicians and, in general, healthcare personnel to frequently face delicate situations that require accurate and careful decision making. The utilization of decision-support models is necessary to improve the decision-making process and to identify the optimal policy. The models allow unveiling of the decisions to be taken, their consequences, and the uncertain events that are involved in the problem. Influence diagrams are graphical tools for the representation and solution of decision-making problems. By representation, one means the identification of the decision-making problem elements. In particular, an influence diagram reveals the probabilistic dependences among the uncertain quantities and the state of information at each decision stage. By solution, one means the determination of the preferred alternative (best strategy selection) given the state of information. Influence diagrams grant decision makers the possibility of representing complex decision-making problems in a thorough albeit compact fashion. It is this strength over other representation techniques that has made the use of influence diagrams widespread in medical applications. This entry is organized as follows. It provides a description of influence diagrams by means of a sample medical example. The analysis of nodes and arcs of influence diagram follows. The discussion of the properties and levels of influence diagrams is offered next. The discussion is a prelude to a synthetic description of influence diagram solution algorithms. The relationship between influence diagrams and decision trees, and a brief mention about other graphical representation techniques, concludes the entry.

Description

An influence diagram is a directed graph composed of nodes and arcs (Figure 1).

The graph is acyclic. There are three types of nodes: decision, chance, and value. A decision node is represented by a rectangular box. A chance node is represented by a circular box. There is one unique value node displayed by a diamond or rhombus; occasionally an octagon is used. Arrows joining nodes are called arcs. The value node ends the diagram, and an influence diagram containing a value node is said to be oriented. In case there are no value nodes and decision nodes, the influence diagram coincides with a Bayesian network.

Figure 1 displays an influence diagram representing the following decision-making problem. A physician must select the treatment for a patient. The first stage of the treatment foresees a choice between Cures A or B. The two cures have a different efficacy and a different cost, with their overall effect strongly dependent on the patient's response. After 1 week, the physician reevaluates the patient's conditions. Depending on the evaluation results, the physician has to decide between continuing with Cure A, switching to B, or resorting to a third cure, C. The problem contains two (sequential) decisions.

Elements

Nodes

An influence diagram contains three types of nodes. Decision nodes display the decisions to be taken at different stages of the decision analysis problem at hand. A variable contained in a decision node is an alternative. The choice among alternatives is under the control of the decision maker, who selects the alternative that maximizes the decision maker's utility. In Figure 1, decision node “Decision 1: A or B?” represents the first selection between Cures A and B; the node “Decision 2: A, B, or C?” represents the selection between Cures A, B, and C. The second selection is made after reeval-uation of the patient's conditions.

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