Medical IT Statistics Exam Topics PDF

Theoretical and practical exam topics: 1. 3 phases of drug action 2. Basic directions of computer-aided drug design (RI and RD) 3. 1D-4D drug models 4. Types of molecular surfaces 5. Receptor theories in molecular modeling 6. Agonist, antagonist, reverse antagonist 7. Receptor and drug...

Theoretical and practical exam topics: 1. 3 phases of drug action 2. Basic directions of computer-aided drug design (RI and RD) 3. 1D-4D drug models 4. Types of molecular surfaces 5. Receptor theories in molecular modeling 6. Agonist, antagonist, reverse antagonist 7. Receptor and drug databases 8. Pharmacophore, 0D and 1D-QSAR 9. Drug lipophilicity as a molecular descriptor 10. Partition coefficient P 11. Lipophilicity constant 12. Construction of an artificial neuron 13. Types of neural networks 14. Supervised learning method (delta rule) 15. Unsupervised learning method (Hebb\'s rule) 16. Kohonen network characteristics 17. Goals of telemedicine 18. Telecardiology -- goals and methods of implementation 19. Measurement scales in statistics for qualitative and quantitative data 20. The concepts of population and statistical sample 21. Characteristics of a representative sample 22. Types of measurement errors in statistics 23. Accuracy vs. precision 4 case studies 24. Measures of location (central tendency) used in statistics advantages and disadvantages 25. Dispersion measures used in statistics advantages and disadvantages 26. Data preprocessing (scaling, centering, standardization) 27. Statistical hypothesis testing steps 28. Determination of the arithmetic mean, weighted arithmetic mean, mode, median for a given medical data vector 29. Comparison of standard deviations in two samples 30. Normal distribution characteristic, 3 sigma rule 31. Confidence interval for the arithmetic mean method of determination 32. Student\'s t-distribution characteristic 33. Error of I and II type comparison 34. Statistical tests, Statistics calculations, critical areas specification 35. Correlation coefficient value range and interpretation 36. Regression analysis method basics Examples of practical part:\ 1. Draw 2D model of aspirine molecules\ 2. Generate 3D model of aspirine 3\. Calculate logP value for aspirine\ 4. Calculate value of Euclidean distance between two vectors x and y according to given formula:\ 5. Calculate standard deviation, mean, median etc. for a given vector x.\ \ Examples of theoretical part: 1\. \ \ \