1. MUDIM (Petr Šimeček, Euromise) system for multidimensional compositional models (Radim Jiroušek) C++ code, distributed as R-package focused on medical applications

2. Contents: idea of conditional independence and (de)composition possible applications of MUDIM expert system data mining STULONG dataset

3. CI - Theory of Storks BIRTH RATE STORK POPULATION

4. CI - Theory of Storks BIRTH RATE STORK POPULATION Statistically connected Do storks deliver newborns?

5. CI - Theory of Storks BIRTH RATE STORK POPULATION ENVIRONMENT No!

6. CI - Theory of Storks BIRTH RATE STORK POPULATION ENVIRONMENT connected

7. CI – Weather WEATHER YESTERDAY WEATHER TOMORROW WEATHER TODAY

8. CI – Weather WEATHER YESTERDAY WEATHER TODAY WEATHER TOMORROW

9. CI – Sample Medical Data = variable (attribute); f.e. AGE, BLOOD PREASURE, …

10. CI – Sample Medical Data (unconditional) statistical connection (correlation) between the pair of variables = = variable (attribute); f.e. AGE, BLOOD PREASURE, …

11. CI – Storks & Weather BIRTH RATE STORK POPULATION ENVIRONMENT YESTERDAY TODAY TOMORROW

12. CI – Storks & Weather BIRTH RATE STORK POPULATION ENVIRONMENT YESTERDAY TODAY TOMORROW

13. CI – Sample Medical Data causality between the pair of variables = = variable (attribute); f.e. AGE, BLOOD PREASURE, …

14. Locality - illustration Variable X Directly explanatory variables for X Other variables If we know information about directly explanatory variables for X, then knowledge about other explanatory variables is useless for predicting X.

15. Applications – Expert Systems Causality

16. Applications – Expert Systems Causality

17. Applications – Expert Systems Causality

18. Applications – Expert Systems Causality

19. Applications – Expert Systems Causality

20. Idea of Compositional Models

21. Applications – Expert Systems Causality What is the distribution ofif we know ?

22. Data Mining We don’t know “anything”, there are lots of variables and lots of possible relations between them. We need to formulate possible hypothesis, suggest some promising models, etc. (useful in pre-research).

23. Data Mining Variables Data

24. Direction of Causality Problem is equivalent to are equivalent, but they are not equivalent to

25. STULONG Dataset = Dataset containing research data on cardiovascular disease (1976-79) 1417 patients (Czech middle-aged men) 244 attributes surveyed with each patient at the entry examination 37 selected attributes are described here

26. (Incomplete) List of Attributes AGE MARITAL STATUS EDUCATION OCCUPATION PHISICAL ACTIVITY TRANSPORT TO JOB SMOKING ALCOHOL TEA AND COFFEE MYOCARDIAL INFARCTION HYPERTENSION ICTUS HYPERLIPIDEMIA CHEST PAIN ASTHMA HEIGHT & WEIGHT BLOOD PREASURE …

27. Graph of Correlated Pairs 464 of 666 possible pairs are statistically connected (p=0.05)

28. Graph of Correlated Pairs 2 160 of 666 possible pairs are statistically connected (p=0.05/666)

29. 56 arrows

30. Risk Factors for Hypertension >summary(glm(HT~HYPLIP+IM+AGE+SUBSC,data=C,family="bino mial")) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.322730 1.274252 -3.392 0.000693 *** IM 1.246937 0.513342 2.429 0.015138 * HYPLIP 1.126383 0.333971 3.373 0.000744 *** SUBSC 0.009521 0.003978 2.393 0.016699 * AGE 0.245182 0.136678 1.794 0.072835. --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

31. Risk Factors for Hypertension Interpretation: HYPERLIPIDEMIA and IM triple odds of ratio Each three years of AGE double odds of ratio There is also small, but evincible connection to skinfold above musculus subscapularis (SUBSC)