Biomedical informatics

Biomedical informatics is a rapidly developing field whose content matter is, above all, the use of computers and information technologies (IT), system approach, statistics and epidemiology and mathematical methodologies in medicine.
Biomedical informatics is experiencing permanent development, and at present it is focused on these major problem areas: the area of support to clinical decision-making, biomedical statistics, robotics, computer modelling, image processing; and the area of medical information systems. In clinical medicine it means the following areas: clinical computing, analyses of images, issues of biosignal, computer modelling, artificial intelligence (including expert systems), support of decision-making, issues of statistics and biometrics, classification in medicine, computers in operating the devices, robotic and artificial organs. In the areas of information system, the issues of their implementation in health care settings are tackled as well as specific questions of data protection and ethical problems and creation of hospital information systems. In the areas of theoretical medicine, it is mainly about modelling of physiologic functions and issues of bioinformatics.

Questions on the state doctoral examination in biomedical informatics
Informatics set
1. Concept of data, information, knowledge, uncertainty and entropy
2. Decision making in medicine, specificity, sensitivity and predictive value
3. Expert systems and artificial intelligence in medicine
4. Use of biomedical information sources
5. Internet in medicine, health information quality assessment
6. Neural networks, Bayesian networks and types of neural networks
7. Decision theory in medicine, decision support systems
8. Cybernetic security, data protection in medicine, electronic signature
9. Hospital information system, medical record, medication record
10. Structure and principles of information systems in healthcare
11. Electronic data networks their hierarchy in healthcare.
12. International classification of diseases
13. Data mining methods
14. Mathematical modeling
15. Evidence-based medicine, translational medicine
16. Clinical studies, principles and classification
17. Therapeutic algorithms and their formalization
18. Biological signals, basic concepts, classification and analysis
19. Image analysis and processing
20. Telemedicine
21. Biomedical informatics outlook
22. Health insurance, economical models of health care
23. National Health Information System

Medical statistics
1. Descriptive characteristics of continuous and categorical random variables, graphical representation of data
2. Population and random sample, location and scale parameter of continuous random variables a its sample estimates, moments of continuous random variables
3. Continuous and discrete probability distributions, normal (Gaussian) and uniform distribution, alternative and binomial distribution
4. Statistical testing – random sample, representative sample, medical hypothesis, null and alternative statistical hypothesis, test statistic, significance level of statistical test, critical value, observed significance level (p-value), statistical software
5. Hypotheses testing and confidence intervals
6. Testing hypothesis about the mean of continuous random variable – parametric one-sample and two-sample tests, paired tests, nonparametric tests
7. Categorial data analysis – Chi-squared test, Fischer test
8. Correlation analysis – correlation and covariance matrix, types of correlation (Pearson, Kendall, Spearman), correlation and causality, uncorrelation vs. independence
9. Time series, time trend, periodicity
10. Multivariate methods – discriminant, factor and cluster analysis, principal components, graphical methods
11. Health statistics and clinical registries
12. Phases of clinical trials I - IV
13. Survival analysis (Kaplan-Meier estimate, Cox PH model and its variants for the case of violated PH assumptions)
14. Linear regression and problem of collinearity of the predictors
15. Analysis of variance
16. Generalised linear regression (logistic regression, Poisson regression)
17. Akaike (AIC) a Bayesian information criterium (BIC), optimal model selection
18. Parametric and nonparametric statistical tests of hypotheses (a general comparison)
19. Multiple statistical tests and inflation of statistical significance level alpha, simultaneous statistical tests
20. Euclidean and Mahalanobis statistical distance
21. Classification methods, regression and classification trees
22. Exploratory and confirmative analysis, meta-analysis
23. Bayes theorem, Bayesian vs. frequentist (classical) statistics

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