1. Judgement and Decision Making in Information Systems Diagnostic Modeling: Bayes’ Theorem, Influence Diagrams and Belief Networks Yuval Shahar, M.D., Ph.D.

2. Reasoning Under Uncertainty Example: Medical Diagnosis Uncertainty is inherent to medical reasoning – relation of diseases to clinical and laboratory findings is probabilistic –Patient data itself is often uncertain with respect to value and time –Patient preferences regarding outcomes vary –Cost of interventions and therapy can change Principles of diagnosis modeling and computation are identical in engineering, finance, scientific evidence

3. Test Characteristics Disease Test result Disease present Disease absent Total Positive True positive (TP) False positive (FP) TP+FP Negative False negative (FN) True negative (TN) FN+TN TP+FNFP+TN

4. Test Performance Measures The gold standard test: the procedure that defines presence or absence of a disease (often, very costly) The index test: The test whose performance is examined True positive rate (TPR) = Sensitivity: –P(Test is positive|patient has disease) = P(T+|D+) –Ratio of number of diseased patients with positive tests to total number of patient: TP/(TP+FN) True negative rate (TNR) = Specificity –P(Test is negative|patient has no disease) = P(T-|D-) –Ratio of number of nondiseased patients with negative tests to total number of patients: TN/(TN+FP)

5. Test Predictive Values Positive predictive value (PV+) = P(D|T+) = TP/(TP+FP) Negative predictive value (PV-) = P(D-|T-) = TN/(TN+FN)

6. Lab Tests: What is “Abnormal”?

7. The Cut-off Value Trade off Sensitivity and specificity depend on the cut off value between what we define as normal and abnormal Assume high test values are abnormal; then, moving the cut-off value to a higher one increases FN results and decreases FP results (i.e. more specific) and vice versa There is always a trade off in setting the cut-off point

8. Receiver Operating Characteristic (ROC) Curves: Examples

9. Receiver Operating Characteristic (ROC) Curves: Interpretation ROC curves summarize the trade-off between the TPR (sensitivity) and the false positive rate (FPR) (1-specificity) for a particular test, as we vary the cut-off threshold The greater the area under the ROC curve, the better (more sensitive, more specific) the index test we are considering

10. Application of Bayes’ Theorem to Diagnosis

11. Odds-Likelihood (Odds Ratio) Form of Bayes’ Theorem Odds = P(A)/(1-P(A)); P = Odds/(1+Odds) Post-test odds = pretest odds * likelihood ratio

12. Application of Bayes’ Theorem Needs reliable pre-test probabilities Needs reliable post-test likelihood ratios Assumes one disease only (mutual exclusivity of diseases) Can be used in sequence for several tests, but only if they are conditionally independent given the disease; then we use the post-test probability of T i as the pre- test probability for T i+1 (AKA Simple, or Naïve, Bayes)

13. Relation of Pre-Test and Post-Test Probabilities

14. Example: Computing Predictive Values Assume P(Downs Syndrom): – (A) 0.1% (age 30) – (B) 2% (age 45) Assume for amniocentesis: Sensitivity is 99%, Specificity is 99%, for Downs Syndrome For both cases, A and B: –PV+ = P(DS|Amnio+) = ?? –PV- = P(DS-|Amnio-) = ??

15. Predictive Values: Down Syndrom

16. Bayesian Diagnostic System Example: de Dombal’s Abdominal-Pain System (1972) Domain: Acute abdominal pain (7 possible diagnoses) Input: Signs and symptoms of patient Output: Probability distribution of diagnoses Method: Naïve Bayesian classification Evaluation: an eight-center study involving 250 physicians and 16,737 patients Results: –Diagnostic accuracy rose from 46 to 65% –The negative laparotomy rate fell by almost half –Perforation rate among patients with appendicitis fell by half –Mortality rate fell by 22% Results using survey data consistently better than the clinicians’ opinions and even the results using human probability estimates!

17. Influence Diagrams: An Alternative, Powerful Tool for Modeling Decisions A graphical notation for modeling situations involving multiple decisions, probabilities, and utilities Computationally: equivalent to decision trees Has several distinct advantages and disadvantages relative to decision trees

18. Influence Diagrams: Node Conventions Chance node Decision node Utility node Deterministic node

19. Link Semantics in Influence Diagrams Dependence link (possible probabilistic relationship) Information link Influence link “No-forgetting” link

20. Decision Trees: an HIV Example Decision node Chance node

21. Influence Diagrams: An HIV Example

22. The Structure of Influence Diagram Links

23. Belief Networks (Bayesian/Causal Probabilistic/Probabilistic Networks, etc) Disease Fever Sinusitis Runny nose Headache Influence diagrams without decision and utility nodes Gender

24. Link Semantics in Belief Networks Dependence Independence Conditional independence of B and C, given A B C A

25. Assessment Versus Observation Orders Usually it is most convenient to consider relationship between diagnoses and tests in assessment order: P(T+|D+) –This is also the easier order in which experts assess the probabilities or in which we can learn them from data However, in real life we need to compute the probability of diseases given observed findings, that is, in observation order: P(D+|T+) –That is the fashion in which diagnostic problems are typically presented, although less easy to compute Thus, we often need to reverse the dependence arc to solve the influence diagram or belief network (or to draw the corresponding decision tree)

26. Assessment Order Representation 0.8 T+ 0.2 T- 0.2 T+ 0.8 T- 0.4 D+ 0.6 D- 0.32 0.08 0.12 0.48 D T P(D  & T  )

27. Observation order Representation: Reversing the Arcs 0.727 D+ 0.273 D- 0.143 D+ 0.857 D- 0.44 T+ 0.56 T- 0.32 0.08 0.12 0.48 D T P(D  & T  )

28. Advantages of Influence Diagrams and Belief Networks: Modeling Implications Excellent modeling tool that supports acquisition from domain experts –Intuitive semantics (e.g., information and influence links) –Explicit representation of dependencies –Representation in assessment (not observation) order –very concise representation of large decision models

29. Advantages of Influence Diagrams and Belief Networks: Computational Implications “Anytime” algorithms available (using probability theory) to compute the distribution of values at any node given the values of any subset of the nodes (e.g., at any stage of information gathering) Explicit support for value of information computations

30. Disadvantages of Influence Diagrams and Belief Networks for Modeling The order of decisions (timing) might be obscured The precise relationship between decisions and available information is hidden within the nodes Highly asymmetric problems might be easier to represent as decision trees –Influence diagrams require using a lot of 0/1 probabilities to represent asymmetry (e.g., if the test is not done, the result is sure to be unknown)

31. Problems in Using Influence Diagrams and Belief Networks for Computations Explicit representation of dependencies often requires acquisition of joint probability distributions (P(A|B,C)) Computation is in general intractable (NP hard), making even moderate-sized problems hard to solve without specialized algorithms Solution of even a relatively simple influence diagram requires the use of a computer and specialized software

32. Examples of Successful Belief- Network Applications In clinical medicine: –Pathological diagnosis at the level of a subspecialized medical expert (Pathfinder) –Endocrinological diagnosis (NESTOR) In bioinformatics: –Recognition of meaningful sites and features in DNA sequences –Educated guess of tertiary structure of proteins