A compendium containing the course material for MM4 through MM10 can be purchased from Lisbeth Palmelund, room F2-24, price 100 DKK.

 

DECISION SUPPORT SYSTEMS

 

Minimodule 4

 

Title:         Causal Probabilistic Networks

 

Literature: Medical Decision Support Systems Based on Causal Probabilistic Networks,

                  Steen Andreassen, chapter 1 pp. 7-14, chapter 2 pp. 15-25.

 

Exercise 1: Calculate P (DçS), from the tables below for P(D) and P (SçD)

 

P (SçD)

D

Yes

No

S

Yes

0,90

0,01

No

0,10

0,99

 

P (D)

Yes

0,001

No

0,999

 

Exercise 2: Calculate P (A), P (B), P (AçB) and P (BçA) from table 2.14 below.

 

Reference: An introduction to Baysian Networks by Finn V. Jensen

Exercise 3: In the graphs below determine which variables are d-connected to A.

 

 

Exercise 4:

a)   Construct the graph (nodes and links) for a Causal Probabilistic Network (CPN) that represents a model of whether or not a car engine will start. Use the same variables as MM1 exercises“Artificial Intelligence and the design of expert systems (see figure)

b)   Produce plausible conditional probability tables for the CPN.

c)   Discuss the choice of a priori probabilities for founding nodes (nodes without parents).

d)   Find two different ways to represent in the CPN that there may be other reasons why the engine will not turn over.

 

 


DECISION SUPPORT SYSTEMS

Minimodule 5

Title:               Propagation of evidence in Causal Probabilistic Nets

Literature:     Steen Andreassen: Medical Decision Support Systems Based on Causal Probabilistic Networks, pp. 25 – 32.

Exercises:

Exercise 1

1.1  Could Quark (Fig. 2.6) have been triangulated differently?
1.2  If so, how would the cliques have looked?
1.3 Draw the junction-tree.

Exercise 2

2.1 Define causal and diagnostic reasoning.
2.2 What does Bayes theorem state about the relation between causal and diagnostic reasoning.

Exercise 3

3.1 Use Hugin to build the “Car won’t start” example. Assume that the prior probabilities:
P(tank-full = “no”) = 0.5%
P(carb_OK = “no”) = 0.1%
P(start_motorOK = “no”) = 0.1%
P(batteryOK = “no”) = 1.0%
P(spark_plugsOK = "no") = 1.0%
Also assume that there is a 1% probability that the engine will not turn over for “other reasons”.

3.2 What is the probability that your car will not start?

3.3 One morning you find that your car will not start. How does that affect your belief in: a) tank-full, b) carb_OK, c) start_motorOK, d) batteryOK and e) lights_work ?

3.4 You note that the lights work. How does that affect your belief in:
a) tank-full , b) carb_OK, c) start_motorOK, d) batteryOK and e) lights_work ?

3.5 Next you note that the engine actually turns when you turn the starter key. How does that affect your belief in:
a) tank-full , b) carb_OK, c) start_motorOK, d) batteryOK and e) lights_work ?

3.6 You have some gas in your spare tank. You put it into the cars gas tank. How does that affect your belief in:
a) tank-full , b) carb_OK, c) start_motorOK, d) batteryOK and e) lights_work ?

3.7 You take out the spark plugs and inspect them. They look just fine. How does that affect your belief in a) tank-full , b) carb_OK, c) start_motorOK, d) batteryOK and e) lights_work ?


DECISION SUPPORT SYSTEMS

 

Minimodule 6

 

Title: Knowledge Acquisition and Representation

 

 

Literature:

Steen Andreassen: Medical Decision Support Systems Based on Causal Probabilistic Networks, pp. 33 – 59.

 

 

Exercises

 

1.         Give some reasons why the introduction of additional nodes in a CPN can give substantial savings in the number of conditional probabilities, as for example in Fig. 3.9.

 

2.         How can the knowledge in MUNIN be checked?

 

3.a       Verify (without Hugin) that the initialisation of Quark (Fig. 2.11) is correct.

 

3.b       What is the marginal probability of FLU and THROAT INF after propagation of FEVER = yes?

 

3.c       Enter and propagate the evidence SORE THROAT = no, under the condition that FEVER = yes. What is the marginal probability of FLU and THROAT INF after propagation of FEVER = yes and SORE THROAT = no?

 


DECISION SUPPORT SYSTEMS

 

Minimodule 7

 

Title: Decisions in CPNs (Decision theory)

 

Literature:

Steen Andreassen: Medical Decision Support Systems Based on Causal Probabilistic Networks, pp. 123 – 138.

 

Exercises

The CPN drawn below represents a situation, where a person may have a Disease. A Treatment is available for that Disease, and the Treatment will influence the probability that the disease is present after the Treatment (DiseaseAfter). The Treatment also has a SideEffect. A Test can be performed. When the Test is performed, a TestResult becomes available. The TestResult depends on whether or not the patient has the Disease. The Test has some complications, that may or may not happen. Utilities are associated with SideEffects, DiseaseAfter and with Complications. Download Hugin Lite (http://www.hugin.com/Products_Services/Products/Demo/Lite/). Download the CPN and make yourself familiar with the conditional probabilities and with the utilities in the CPN.

 

Exercises:

 

1)     Set Test= no. Use Hugin to determine the utility of Treatment = yes and Treatment = no, respectively

2)     How large is the contribution of SideEffects to the utility, in the situation Test= no. and Treatment = yes.

3)      Now we will explore if it is worthwhile to perform the Test, before deciding on Treatment.

a.      To explore this set Test = yes, and read the probability of TestResult.

b.     Further, set TestResult to yes and read the utilities of Treatment = yes and no.

c.      Set TestResult to no and read the utilities of Treatment = yes and no.

d.     Use the results collected above to construct a decision tree similar to the one in Fig. 6.6 in Medical Decision Support Systems Based on Causal Probabilistic Networks.

e.      Is it worthwhile to perform the Test, before deciding on Treatment?


DECISION SUPPORT SYSTEMS

 

Minimodule 8

 

Title: An example of a decision support system: The Diabetes Insulin Advisory System (DIAS)

 

Literature:

Steen Andreassen: Medical Decision Support Systems Based on Causal Probabilistic Networks, pp. 59-68, 111, 140-144.

Orsini Federici et al. Evaluation of a decision support system (DIAS) for the adjustment of the insulin doses in type 1 diabetic patients.

 

Exercises

 

A demonstration of DIAS will be given in the lecture room.


DECISION SUPPORT SYSTEMS

 

Minimodule 9

 

Title:   Learning in CPN’s

 

Literature:

A Tutorial on Learning With Bayesian Networks, David Heckerman, Chapter 1 – 6 pp. 1-23.

A note on updating of conditional probabilities in Bayesian networks, Kristian G. Olesen and Steen Andreassen, pp. 1-7.

 

Exercises: To be decided, possibly a demo.


DECISION SUPPORT SYSTEMS

 

Minimodule 10

 

Title:   An example of a decision support system: Antibiotic treatment of severe infections - TREAT

 

 

Literature:     Steen Andreassen et al. 2004: A probabilistic network for fusion of data and knowledge in clinical microbiology

 

Exercises: A demonstration of Treat will be given in the lecture room.