Table 1.

A formal description of the ITS methodology for suggesting orders via a Bayesian network. This parallels the graphical example in Figure 2.

Algorithm: Iterative Treatment Suggestion (ITS)

Where:

G is a Bayesian Network Model

O is a set of possible orders, initially including all orders in G

D is a set of possible diagnoses, including all diagnoses in G

E is a set of evidence, initially containing all D set to false

Do:

1. Update beliefs (compute the posterior probability of all O ∉ E).

2. Create a list of all O ∉ E in descending order of posterior probability, optionally stopping at a predefined threshold.

3. Display the list and D to the user and wait for the user to choose an order or diagnosis from the list.

4. Move the order from O to E, or set the diagnosis to true in E.

Until the user closes the session